File size: 196,922 Bytes
6fa4bc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997 2998 2999 3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097 3098 3099 3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197 3198 3199 3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297 3298 3299 3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397 3398 3399 3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413 3414 3415 3416 3417 3418 3419 3420 3421 3422 3423 3424 3425 3426 3427 3428 3429 3430 3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483 3484 3485 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497 3498 3499 3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597 3598 3599 3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655 3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697 3698 3699 3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797 3798 3799 3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 3882 3883 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897 3898 3899 3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955 3956 3957 3958 3959 |
{
"paper_id": "2022",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T06:07:11.629978Z"
},
"title": "Assessment of Massively Multilingual Sentiment Classifiers",
"authors": [
{
"first": "Krzysztof",
"middle": [],
"last": "Rajda",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Wroc\u0142aw University of Science and Technology",
"location": {
"addrLine": "2 Brand24 AI"
}
},
"email": "[email protected]"
},
{
"first": "\u0141ukasz",
"middle": [],
"last": "Augustyniak",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Wroc\u0142aw University of Science and Technology",
"location": {
"addrLine": "2 Brand24 AI"
}
},
"email": "[email protected]"
},
{
"first": "Piotr",
"middle": [],
"last": "Gramacki",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Wroc\u0142aw University of Science and Technology",
"location": {
"addrLine": "2 Brand24 AI"
}
},
"email": "[email protected]"
},
{
"first": "Marcin",
"middle": [],
"last": "Gruza",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Wroc\u0142aw University of Science and Technology",
"location": {
"addrLine": "2 Brand24 AI"
}
},
"email": "[email protected]"
},
{
"first": "Szymon",
"middle": [],
"last": "Wo\u017aniak",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Wroc\u0142aw University of Science and Technology",
"location": {
"addrLine": "2 Brand24 AI"
}
},
"email": "[email protected]"
},
{
"first": "Tomasz",
"middle": [],
"last": "Kajdanowicz",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Wroc\u0142aw University of Science and Technology",
"location": {
"addrLine": "2 Brand24 AI"
}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Models are increasing in size and complexity in the hunt for SOTA. But what if those 2% increase in performance does not make a difference in a production use case? Maybe benefits from a smaller, faster model outweigh those slight performance gains. Also, equally good performance across languages in multilingual tasks is more important than SOTA results on a single one. We present the biggest, unified, multilingual collection of sentiment analysis datasets. We use these to assess 11 models and 80 high-quality sentiment datasets (out of 342 raw datasets collected) in 27 languages and included results on the internally annotated datasets. We deeply evaluate multiple setups, including fine-tuning transformer-based models for measuring performance. We compare results in numerous dimensions addressing the imbalance in both languages coverage and dataset sizes. Finally, we present some best practices for working with such a massive collection of datasets and models from a multilingual perspective.",
"pdf_parse": {
"paper_id": "2022",
"_pdf_hash": "",
"abstract": [
{
"text": "Models are increasing in size and complexity in the hunt for SOTA. But what if those 2% increase in performance does not make a difference in a production use case? Maybe benefits from a smaller, faster model outweigh those slight performance gains. Also, equally good performance across languages in multilingual tasks is more important than SOTA results on a single one. We present the biggest, unified, multilingual collection of sentiment analysis datasets. We use these to assess 11 models and 80 high-quality sentiment datasets (out of 342 raw datasets collected) in 27 languages and included results on the internally annotated datasets. We deeply evaluate multiple setups, including fine-tuning transformer-based models for measuring performance. We compare results in numerous dimensions addressing the imbalance in both languages coverage and dataset sizes. Finally, we present some best practices for working with such a massive collection of datasets and models from a multilingual perspective.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Multilingual text representations are becoming increasingly important in science as well as the business community. However how universal and versatile they truly are? Can we use them to train one, multilingual, production-ready sentiment classifier? To verify this research question, we gathered a massive collection of sentiment analysis datasets and evaluated 11 different models on them. We want to assess the performance of fine-tuning languages models as well as language models as feature extractors for simpler, even linear models.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Sentiment analysis is subjective and both domain and language-dependent, hence there is an even greater need to understand the behaviour and performance of the multilingual setup. We focused on multilingual sentiment classification because our business use cases involve the analysis of texts in multiple languages across the world. Moreover, one universal model in a production environment is much easier to deploy, maintain, monitor, remove biases or improve the model's fairness -especially in cases when the load differs between languages and could change over time. We want to compare state-of-the-art multilingual embedding methods and select the ones with the best performance across languages.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The main objective of this article is to answer the following Research Questions: (RQ1) Are we able to create a single multilingual sentiment classifier, that performs equally well for each language? (RQ2) Does fine-tuning of transformer-based models significantly improve sentiment classification results? (RQ3) What is the relationship between model size and performance? Is bigger always better?",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our main contribution includes 3 main points. Firstly, we perform a large scoping review of published sentiment datasets. Using a set of rigid inclusion and exclusion criteria, we filter the initial pool of 342 datasets down to 80 high-quality datasets representing 27 languages. Secondly, we evaluated how universal and versatile multilingual text representations are for the sentiment classification problem. Finally, we compared many deep learningbased approaches with fine-tuning and without it for multilingual sentiment classification.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The remainder of this paper is organized as follows: Section 2 presents a literature review on the topic of multilingual sentiment analysis; Section 3 describes the language models, datasets, and our evaluation methodology; Section 4 describes the conducted experiments and summarizes the results; Section 5 discusses the results in terms of research questions; Section 6 presents conclusions and describes further works.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Multilingual Text Representations. Initially, multilingual text representations were obtained using multilingual word embeddings (Ruder et al., 2019) . These were created using various training techniques, parallel corpora, and dictionaries, for example by aligning the monolingual Word2Vec (Mikolov et al., 2013a) vector spaces with linear transformations using small parallel dictionaries (Mikolov et al., 2013b) . To better represent longer texts, modern approaches use more complex contextual language models like BiLSTM (Artetxe and Schwenk, 2019) and Transformers (Feng et al., 2020; Conneau et al., 2020; Devlin et al., 2019; Xue et al., 2021; . Their multilingual capabilities result from pretraining on multilingual objective tasks like machine translation (Artetxe and Schwenk, 2019) , translation language modelling (TLM) (Conneau et al., 2020; Conneau and Lample, 2019) or translation ranking (Feng et al., 2020; Yang et al., 2019) . Details of the models used in our experiments are described in Section 3.1.",
"cite_spans": [
{
"start": 129,
"end": 149,
"text": "(Ruder et al., 2019)",
"ref_id": "BIBREF53"
},
{
"start": 291,
"end": 314,
"text": "(Mikolov et al., 2013a)",
"ref_id": "BIBREF40"
},
{
"start": 391,
"end": 414,
"text": "(Mikolov et al., 2013b)",
"ref_id": "BIBREF41"
},
{
"start": 525,
"end": 552,
"text": "(Artetxe and Schwenk, 2019)",
"ref_id": "BIBREF3"
},
{
"start": 570,
"end": 589,
"text": "(Feng et al., 2020;",
"ref_id": "BIBREF25"
},
{
"start": 590,
"end": 611,
"text": "Conneau et al., 2020;",
"ref_id": null
},
{
"start": 612,
"end": 632,
"text": "Devlin et al., 2019;",
"ref_id": "BIBREF22"
},
{
"start": 633,
"end": 650,
"text": "Xue et al., 2021;",
"ref_id": null
},
{
"start": 766,
"end": 793,
"text": "(Artetxe and Schwenk, 2019)",
"ref_id": "BIBREF3"
},
{
"start": 833,
"end": 855,
"text": "(Conneau et al., 2020;",
"ref_id": null
},
{
"start": 856,
"end": 881,
"text": "Conneau and Lample, 2019)",
"ref_id": "BIBREF17"
},
{
"start": 905,
"end": 924,
"text": "(Feng et al., 2020;",
"ref_id": "BIBREF25"
},
{
"start": 925,
"end": 943,
"text": "Yang et al., 2019)",
"ref_id": "BIBREF77"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "The quality of multilingual text representations is usually evaluated with cross-and multilingual tasks like cross-lingual natural language inference (Conneau et al., 2018) , question answering (Lewis et al., 2020) , named entity recognition (Tjong Kim Sang, 2002; Tjong Kim Sang and De Meulder, 2003) or parallel text extraction (Zweigenbaum et al., 2017; Ziemski et al., 2016 ). Another important benchmark is XTREME (Hu et al., 2020) , which is designed for testing the abilities of cross-lingual transfer across 40 languages and 9 tasks. Despite its massive character, XTREME lacks benchmarking task of sentiment analysis, also only mBERT, XLM, XLM-R, and MMTE are used as baseline models. We try to fill this gap with our work. K et al. (2020) performed extensive research on the cross-lingual ability of mBERT. Wu and Dredze (2020) compared mBERT with monolingual models and found that it under-performs on low-resource languages. Liu et al. (2020) analyzed a cross-lingual ability of mBERT considering a contextual aspect of mBERT and dataset size. There is a significant lack of detailed analysis of characteristics of other language models, despite mBERT.",
"cite_spans": [
{
"start": 150,
"end": 172,
"text": "(Conneau et al., 2018)",
"ref_id": "BIBREF18"
},
{
"start": 194,
"end": 214,
"text": "(Lewis et al., 2020)",
"ref_id": "BIBREF36"
},
{
"start": 242,
"end": 264,
"text": "(Tjong Kim Sang, 2002;",
"ref_id": "BIBREF70"
},
{
"start": 265,
"end": 301,
"text": "Tjong Kim Sang and De Meulder, 2003)",
"ref_id": "BIBREF72"
},
{
"start": 330,
"end": 356,
"text": "(Zweigenbaum et al., 2017;",
"ref_id": "BIBREF79"
},
{
"start": 357,
"end": 377,
"text": "Ziemski et al., 2016",
"ref_id": "BIBREF78"
},
{
"start": 419,
"end": 436,
"text": "(Hu et al., 2020)",
"ref_id": "BIBREF28"
},
{
"start": 937,
"end": 954,
"text": "Liu et al. (2020)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Multilingual Sentiment Analysis. In literature, there are several examples of reviews, which focus on traditional sentiment analysis methods (e.g., lexicon-based, lexical features engineering, shallow models), while not mentioning any embeddingbased methods (Dashtipour et al., 2016; Sagnika et al., 2020) . They are a valuable source of information about sentiment datasets. However, modern NLP applications often utilize deep learning techniques, which were not covered there. An example of a deep learning-based approach was presented by Attia et al. (2018) , who trained a convolutional neural network (CNN) on word-level embeddings of texts in English, German and Arabic, a separate model for each language. This approach requires many resources and computations as one has to create a separate embedding dictionary for each language. An alternative approach is to use characterlevel embeddings. Wehrmann et al. (2017) trained such a model to classify tweets written in English, German, Portuguese, and Spanish as either positive or negative. This approach requires fewer parameters than word embedding models.",
"cite_spans": [
{
"start": 258,
"end": 283,
"text": "(Dashtipour et al., 2016;",
"ref_id": "BIBREF20"
},
{
"start": 284,
"end": 305,
"text": "Sagnika et al., 2020)",
"ref_id": "BIBREF55"
},
{
"start": 541,
"end": 560,
"text": "Attia et al. (2018)",
"ref_id": "BIBREF4"
},
{
"start": 901,
"end": 923,
"text": "Wehrmann et al. (2017)",
"ref_id": "BIBREF73"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Newer approaches to multilingual sentiment analysis use deep models and machine translation e.g. Can et al. (2018) trained a Recurrent Neural Network (RNN) on English reviews and evaluated it on machine-translated reviews in Russian, Spanish, Turkish and Dutch. They used the Google Translation API and pre-trained GloVe embeddings for English. Kanclerz et al. (2020) used LASER sentence embeddings to learn a sentiment classifier on Polish reviews and used this classifier to predict sentiment on reviews translated into other languages. As we can see most of the research covers only a couple of languages for sentiment analysis. Hence, we decided to gather a massive collection of 342 datasets in 27 languages.",
"cite_spans": [
{
"start": 345,
"end": 367,
"text": "Kanclerz et al. (2020)",
"ref_id": "BIBREF32"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "We conducted several experiments to answer if there is a truly universal multilingual text representation model (Table 1 ). We tested their performance based on the largest sentiment analysis dataset in the literature.",
"cite_spans": [],
"ref_spans": [
{
"start": 112,
"end": 120,
"text": "(Table 1",
"ref_id": "TABREF0"
}
],
"eq_spans": [],
"section": "Evaluation Methodology",
"sec_num": "3"
},
{
"text": "We used multiple language models as text representation methods (Table 1) . We aimed to select models varied in terms of architecture, size, and type of data used in pre-training. We selected two models which do not use transformer architecture (CNN and BiLSTM) as a baseline. We also used models, based on multiple different transformer architec- tures (T5, BERT, RoBERTa). We also included models' trained with multilingual knowledge distillation (Reimers and Gurevych, 2020) ",
"cite_spans": [
{
"start": 449,
"end": 477,
"text": "(Reimers and Gurevych, 2020)",
"ref_id": "BIBREF50"
}
],
"ref_spans": [
{
"start": 64,
"end": 73,
"text": "(Table 1)",
"ref_id": "TABREF0"
}
],
"eq_spans": [],
"section": "Multilingual Language Models",
"sec_num": "3.1"
},
{
"text": "such as paraphrase-xlm-r-multilingual-v1 (XLM-R-dist), distiluse-base-multilingual-cased-v2 (mUSE-dist), paraphrase-multilingual-mpnet-base-v2 (MPNet).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Multilingual Language Models",
"sec_num": "3.1"
},
{
"text": "We also included models trained on multilingual corpus like Wikipedia (Wiki) or Common Crawl (CC) as well as models trained with the use of parallel datasets. Selected models differ in size -from LASER with 52M parameters to LaBSE with 470M. They also differ regarding covered languages, from 16 up to more than a hundred. By a number of languages, we mean how many were used to create a specific model, not all languages supported by the model (an example is MPNet, trained using 53 languages, but as it is based on XLM-R, it supports 100). We also compared inference time which was calculated as a mean of inference times of 500 randomly selected texts samples from all datasets. The hardware used is described in Section A.1. We searched for models comparison in similar tasks in literature but failed to find any, which compares more than 2 or 3 models. All models used are characterized in Table 1 .",
"cite_spans": [],
"ref_spans": [
{
"start": 895,
"end": 902,
"text": "Table 1",
"ref_id": "TABREF0"
}
],
"eq_spans": [],
"section": "Multilingual Language Models",
"sec_num": "3.1"
},
{
"text": "We gathered 342 sentiment analysis datasets containing texts from multiple languages, data sources and domains to check our research questions. We searched for datasets in various sources, like Google Scholar, GitHub repositories, and the Hug-gingFace datasets library. Such a large number of datasets allows us to estimate the quality of lan-guage models in various conditions with greater certainty. To the best of our knowledge, this is the largest sentiment analysis datasets collection currently gathered and researched in literature. After preliminary analysis, we selected 80 datasets of reasonable quality based on 5 criteria. 1We rejected datasets containing weak annotations (e.g., datasets with labels based on emoji occurrence or generated automatically through classification by machine learning models), as our analysis showed that they may contain too much noise (Northcutt et al., 2021) . 2We reject datasets without sufficient information about the annotation procedure (e.g., whether annotation was manual or automatic, number of annotators) because it is always a questionable decision to merge datasets created with different annotation guidelines. 3We accepted reviews datasets and mapped their rating labels to sentiment values. The mapping rules are described in section 3.2.1. 4We rejected 2-class only datasets (positive/negative without neutral), as our analysis showed their low quality in terms of 3-class usage. (5) Some datasets contain samples in multiple languages -we split them and treated each language as a separate dataset.",
"cite_spans": [
{
"start": 878,
"end": 902,
"text": "(Northcutt et al., 2021)",
"ref_id": "BIBREF47"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "3.2"
},
{
"text": "Working with many datasets means that they could contain different types of text, various artefacts such as URL or HTML tags, or just different sentiment classes mappings. We applied a couple of preprocessing steps to each dataset to unify all datasets. We dropped duplicated texts. We removed URLs, Twitter mentions, HTML tags, and emails. During an exploratory analysis, we spotted that reviewbased datasets often contain many repeated texts with contradictory sentiment scores. We deduplicated such cases and applied a majority voting to choose a sentiment label. Finally, we unified labels from all datasets into 3-class (negative, neutral, positive). In the case of datasets containing user ratings (on a scale of 1-5) along with their review texts, we mapped the ratings to sentiment as follows: the middle value (3) of the rating scale was treated as a neutral sentiment, ratings below the middle as negative sentiment, and ratings above the middle as positive sentiment.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data Preprocessing",
"sec_num": "3.2.1"
},
{
"text": "Presenting statistics of 80 datasets across 27 languages could be challenging. We checked different aggregating and sorting of datasets to make their statistics as readable as possible and easily usable for results discussion. We decided to group datasets by their language and next sorted them based on the number of examples in every aggregate - Table 2 . In total, we selected 80 datasets containing 6,164,942 text samples. Most of the texts in the datasets are in English (2,330,486 samples across 17 datasets), Arabic (932,075 samples across 9 datasets), and Spanish (418,892 samples across 5 datasets). The datasets contain text from various categories: social media (44 datasets), reviews (24 datasets), news (5 datasets), and others (7 datasets). They also differ in the mean number of words and characters in examples. See the detailed information of datasets used is in Tables 5 and 6. We also selected around 60k samples for training and validation and another 60k for testing. This is enough for training a small classifier on top of a frozen embedding or fine-tuning a transformerbased model (see Section 3.3). This was also done due to computation resources limitations.",
"cite_spans": [],
"ref_spans": [
{
"start": 348,
"end": 355,
"text": "Table 2",
"ref_id": "TABREF1"
},
{
"start": 880,
"end": 895,
"text": "Tables 5 and 6.",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "Data Preprocessing",
"sec_num": "3.2.1"
},
{
"text": "We have also used an internal dataset that was manually annotated. It is multi-domain and consists of texts from various Internet sources in Polish and English. It includes texts from social media, news sites, blogs and forums. We used this dataset as a gold standard. We need it because we do not know exact annotation guidelines from literature datasets and we assume that those guidelines differed between datasets. In our gold dataset, each text was annotated by 3 annotators with majority label selection. The annotators achieved a high agreement measured by Cohen's kappa: 0.665 and Krippendorff's alpha: 0.666. Statistics of this dataset are presented in Table 3 . All samples were trimmed to the length of 350 chars (mean length of 145 chars).",
"cite_spans": [],
"ref_spans": [
{
"start": 662,
"end": 669,
"text": "Table 3",
"ref_id": "TABREF2"
}
],
"eq_spans": [],
"section": "Internal Dataset",
"sec_num": "3.2.2"
},
{
"text": "We wanted to compare multilingual models in different use cases. Firstly, we wanted to see how much information is stored in pre-trained embedding. In this scenario, we used each of the text representations models listed in Section 3.1 as a feature extractor and coupled them with only a small linear classification head. We used an average from a final layer as a text representation. We will refer to this scenario Just Head -Linear. In the second scenario, we replaced a linear classifier with a BiL-STM classifier, still using the text representation model as a feature extractor. We fed BiLSTM layer with outputs from the last layer of the feature extractor (Just Head -BiLSTM). LASER and mUSE do not provide per-token embeddings and therefore, were not included in this scenario. Since most of our models are transformer-based, we decided to test them in a fine-tuning setup. This last scenario evaluated the fine-tuning of all transformer-based models (referred to as fine-tuning), with an exception made for mUSE-transformer because it was not possible to do with our implementation in Py-Torch with Huggingface models.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Scenarios",
"sec_num": "3.3"
},
{
"text": "For each scenario, we prepared 3 test metrics, which we refer to as a whole test, average by dataset and internal. Each of them separately measures model performance but all of them are based on a macro F1-score. The whole test is calculated on all samples from datasets described in 3.2 combined. It is meant to reflect the real-life performance of a model because our real-world applications often deal with an imbalance in languages distribution (with English being the most popular language used on the Internet). On average by dataset, we first calculate the macro F1-score on each dataset and then calculate the average of those scores. This is meant to show whether the model was not too over-fitted for the majority of languages or the biggest datasets. Finally, in the internal scenario, we assess them on our internal dataset (described in 3.2.2) to measure performance in our domain-specific examples.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Scenarios",
"sec_num": "3.3"
},
{
"text": "To show how each model performs in a bird's eye view, we prepared Nemenyi diagrams (Nemenyi, 1963) for all three experimental setups. Nemeneyi post-hoc statistical test finds groups of models that differ. It was used on the top of multiple comparisons Friedman test (Dem\u0161ar, 2006) . The Nemeneyi test makes a pair-wise comparison of all model's ranks. We used alpha equal to 5%. The Nemeneyi test provides critical distance for compared groups that are not significantly different from each other.",
"cite_spans": [
{
"start": 83,
"end": 98,
"text": "(Nemenyi, 1963)",
"ref_id": "BIBREF45"
},
{
"start": 266,
"end": 280,
"text": "(Dem\u0161ar, 2006)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation Procedure",
"sec_num": "3.4"
},
{
"text": "For each scenario, we adjusted hyperparameters. The hidden size was set to LM's embedding size for linear and fine-tuning and 32 for BiLSTM. By hidden size, we mean middle linear layer size, or in the case of BiLSTM -its hidden size parameter. BiLSTM uses a smaller hidden size because our experiments showed that it does not hurt performance but increases efficiency. The learning rate was initially the same for all scenarios, at the well-established value of 1e-3. We then modified it for each version by decreasing it for fine-tuning (to 1e-5) and slightly increasing it for BiLSTM based model (5e-3). The batch size was determined by our GPU's memory size. We used 200 for linear and BiLSTM and 6 for fine-tuning. We used dropout in classification head -0.5 for BiLSTM and 0.2 for other scenarios. We trained our models for 5 epochs in the fine-tuning scenario and 15 in two others, as those were the max number of epochs before the models started overfitting. We tested with the best F1-score on a validation dataset.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Models Setup",
"sec_num": "3.5"
},
{
"text": "We divided our results into three layers. Firstly, we show a general bird's eye view of all models -it helps to spot the best and the worst models. Then, we provide detailed results for each model aggregated per dataset. Finally, to dig deeper into the model's performance, we show numerical results for each model for each language.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "4"
},
{
"text": "There is no significantly best embedding model in any of the tested scenarios based on the Nemenyi diagrams - Figure 1 . However, we can see that the MPNet proved to be the best (for the linear scenario) and not significantly worse than the best -XLR-M model -in the other two scenarios. It is also worth mentioning that mBERT-based models (mBERT and DistillmBERT) proved to be the worst language models for our tasks.",
"cite_spans": [],
"ref_spans": [
{
"start": 110,
"end": 118,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Bird's Eye View",
"sec_num": "4.1"
},
{
"text": "All models achieve better results with fine-tuning (up to 0.7 F1-score) than with extraction of vectors from text and then applying linear (up to 0.61) or BiLSTM (up to 0.64) layers, shows Table 4. The performance gains are higher when fine-tuning models pretrained on MLM and TLM tasks (like mBERT or XLM-R) compared to mod- els, which were trained with sentence classification tasks, sentence similarity tasks or similar (like LaBSE). For example, mBERT had gains of 9, 11, and 14 percentage points (pp) on whole test, average by dataset and internal test cases, Distilm-BERT -9, 13 and 20pp, XLM-R -6, 10, and 15pp. At the same time, LaBSE had only 6, 8, and 7pp and MPNet -4, 7, 4pp. Still, those models achieve better overall performance. Fine-tuning reduces inequalities in the results between models (0.55 vs 0.43 for best and worst models in Just head -Linear setup, and 0.62 vs 0.56 after Fine-tuning for average by dataset metric). Those results were meant to show a general comparison between fine-tuned models against training just classification head.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Aggregated by Dataset",
"sec_num": "4.2"
},
{
"text": "The additional BiLSTM layer on top of transformer token embeddings improves the results of the model with only a linear layer in most cases. The differences are most clear in the case of the results for our internal dataset, where the result improved even by 13pp. (from 50% to 63%) for the mT5 model.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Aggregated by Dataset",
"sec_num": "4.2"
},
{
"text": "Those results show, that three models are the most promising choices: XLM-R, LaBSE and MP-Net. They achieve comparable performance in all scenarios and test cases. Furthermore, they are better than other models in almost all test cases. XLM-R-dist was very close to those, but analysis with Nemenyi diagrams shows that it is slightly worse than those three. Legend: lang -averaged by all languages, ds -averaged by dataset, ar -Arabic, bg -Bulgarian, bs -Bosnian, cs -Czech, de -German, en -English, es -Spanish, fa -Persian, fr -French, he -Hebrew, hi -Hindi, hr -Croatian, hu -Hungarian, it -Italian, ja -Japanese, lv -Latvian, pl -Polish, pt -Portuguese, ru -Russian, sk -Slovak, sl -Slovenian, sq -Albanian, sr -Serbian, sv -Swedish, th -Thai, ur -Urdu, zh -Chinese.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Aggregated by Dataset",
"sec_num": "4.2"
},
{
"text": "We assessed the performance of each model in each experimental scenario concerning the language. The texts were sub-sampled with stratification by language and class label so that language distribution in the test dataset reflects this in the whole dataset. It means that some languages are underrepresented. We also include the total Macro F1 score value in column \"all\". Results are presented in Figure 2 for fine-tuning scenario and in Figure 5 for others. Those results confirm conclusions from the previous section about the advantage of XLM-R, LaBSE and MPNet. They have the best performance in most languages and together with XLM-R-dist, there are no big differences between them.",
"cite_spans": [],
"ref_spans": [
{
"start": 398,
"end": 406,
"text": "Figure 2",
"ref_id": "FIGREF1"
},
{
"start": 439,
"end": 448,
"text": "Figure 5",
"ref_id": null
}
],
"eq_spans": [],
"section": "Every Model for Every Language",
"sec_num": "4.3"
},
{
"text": "RQ1: Are we able to create a single multilingual sentiment classifier, performing equally well for each language? When considering only the best models (XLM-R, LaBSE, MPNet) in the fine-tuning setup, we observed that they achieve best or close to best results in every language - Figure 5 . In some languages, results are significantly worse than in others, but this is also true for other models evaluated as it may be caused by differences in the number of samples, quality, and difficulty of samples in those languages. Therefore, we can say that one model can work exceptionally well in all languages. On the other hand, statistical analysis which is presented in the form of Nemenyi diagrams in Figures 1a, 1b and 1c showed that there is no statistical difference between top models in fine-tuning setup, so it is not possible to state which of those is the best one. We can rather state which group of models proved to be significantly better than others.",
"cite_spans": [],
"ref_spans": [
{
"start": 280,
"end": 288,
"text": "Figure 5",
"ref_id": null
},
{
"start": 700,
"end": 721,
"text": "Figures 1a, 1b and 1c",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Discussion",
"sec_num": "5"
},
{
"text": "RQ2: Does fine-tuning of transformer-based models significantly improve sentiment classification results? All models worked better when fine-tuned, but the performance gain varied from one to another. It was between 4 (mUSE-dist) and 9 (mBERT and DistilmBERT) pp. F1 on the benchmark test dataset, and between 0 (mUSE-dist) and 20 pp. (DistilmBERT) on our internal dataset. The 17, 15, and 14 pp. gain of mT5, XLM-R, and DistilmBERT on the internal dataset is also worth noting. In general, the most significant gain can be observed in models trained on language modelling only (MLM or TLM), such as XLM-R and mBERT.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "5"
},
{
"text": "The results of our experiments showed that there exists a correlation between the classification result of the language model with its number of parameters. Figure 3 shows that, for all scenarios and test dataset types, bigger models achieve better performance in most cases. However, there are some counterexamples, e.g., mUSE-dist is smaller than mBERT but achieves better performance in Just head -Linear setup, for all dataset types. This indicates that the size of the model is an important factor in its performance, but other factors, like the domain and the type of pretraining task, may also affect the results. Moreover, we observed that this correlation is weaker after fine-tuning. We can often find the model with similar performance to the best one but significantly smaller and faster for the production environment.",
"cite_spans": [],
"ref_spans": [
{
"start": 157,
"end": 165,
"text": "Figure 3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "RQ3: What is the relationship between model size and performance? Is bigger always better?",
"sec_num": null
},
{
"text": "Your Dataset Splits Matter To determine which model works best, we repeated fine-tuning five times to remove a right/wrong random seed factor for each model and dataset subsampling. Due to computation resources limitations, we selected eight models available in Huggingface for finetuning. Interestingly, we can see that one of the samples looks like the outlier - Figure 4 for almost all the evaluated models. The F1-score for this sample is even 4 percentage points worse than other samples' scores. We investigated this anomaly and spotted that it is always the same sample (the same seed for sample generation). As a reminder, since we collected a massive dataset and had limited computational resources, we sub-sampled texts for each of the five runs. Sub-samples between different models stay the same. It looks like the mentioned sample was more difficult than others or had distinctive characteristics. It is hard to tell why without in-depth analysis, hence we intend to conduct further research on the topic of data quality in sentiment analysis tasks using techniques like noise ratio (Northcutt et al., 2021) or data cartography (Swayamdipta et al., 2020) . Here, we see an outstanding example of how vital the dataset's preparation could be regarding split for train/dev/test sets.",
"cite_spans": [
{
"start": 1096,
"end": 1120,
"text": "(Northcutt et al., 2021)",
"ref_id": "BIBREF47"
},
{
"start": 1141,
"end": 1167,
"text": "(Swayamdipta et al., 2020)",
"ref_id": "BIBREF67"
}
],
"ref_spans": [
{
"start": 365,
"end": 373,
"text": "Figure 4",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "RQ3: What is the relationship between model size and performance? Is bigger always better?",
"sec_num": null
},
{
"text": "In this work we evaluated multilingual text representations for the task of sentiment classification by comparing multiple approaches, using different deep learning methods. In the process, we gathered the biggest collection of multi-lingual sentiment datasets -80 datasets for 27 languages. We evaluated 11 models (language models and text vectorization techniques) in 3 different scenarios. We found out that it is possible to create one model which achieves the best or most competitive results in all languages in our collected dataset, but there is no statistical difference between top-performing models. We found out that there is a significant benefit from fine-tuning transformer-based language models and that a model size is correlated with performance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Further Works",
"sec_num": "6"
},
{
"text": "While conducting experiments we identified further issues which we find worth addressing. Dataset quality assessment is in our opinion the most important one and we are planning to address it in further works. Meanwhile, we used datasets with a literature background and trust that they were carefully prepared and have decent quality annotations. We also found out that it is difficult to propose a coherent experiments methodology with such imbalance in languages and datasets sizes. Moreover, analyzing results is difficult, when one must address dimensions of datasets, languages, data sources, models, and experiments scenarios.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Further Works",
"sec_num": "6"
},
{
"text": "Finally, we found out that when sub-sampling a dataset for experiments, seeds play a significant role (see results in Figure 4 ). To analyze this phenomenon, we intend to launch further research and use noise ratio (Northcutt et al., 2021) and data cartography (Swayamdipta et al., 2020) to understand how this split differs from the others. This will be, in our opinion, a good start to a comprehensive analysis of datasets quality for the multi-lingual sentiment classification task which we intend to perform. (Nabil et al., 2015) ar SM Yes 3224 50.9/25.0/24.1 16 94 (Salameh et al., 2015) ar SM Yes 1199 48.0/10.5/41.5 11 51 (Salameh et al., 2015) ar SM Yes 1998 67.5/10.1/22.4 20 107 (Habernal et al., 2013) cs R No 91140 32.4/33.7/33.9 50 311 (Habernal et al., 2013) cs R No 92758 7.9/23.4/68.7 20 131 (Habernal et al., 2013) cs SM Yes 9752 20.4/53.1/26.5 10 59 (Habernal et al., 2013) cs Figure 5: Detailed results of models' comparison. Legend: lang -averaged by all languages, ds -averaged by dataset, ar -Arabic, bg -Bulgarian, bs -Bosnian, cs -Czech, de -German, en -English, es -Spanish, fa -Persian, fr -French, he -Hebrew, hi -Hindi, hr -Croatian, hu -Hungarian, it -Italian, ja -Japanese, lv -Latvian, pl -Polish, pt -Portuguese, ru -Russian, sk -Slovak, sl -Slovenian, sq -Albanian, sr -Serbian, sv -Swedish, th -Thai, ur -Urdu, zh -Chinese.",
"cite_spans": [
{
"start": 215,
"end": 239,
"text": "(Northcutt et al., 2021)",
"ref_id": "BIBREF47"
},
{
"start": 261,
"end": 287,
"text": "(Swayamdipta et al., 2020)",
"ref_id": "BIBREF67"
},
{
"start": 513,
"end": 533,
"text": "(Nabil et al., 2015)",
"ref_id": "BIBREF43"
},
{
"start": 570,
"end": 592,
"text": "(Salameh et al., 2015)",
"ref_id": "BIBREF56"
},
{
"start": 629,
"end": 651,
"text": "(Salameh et al., 2015)",
"ref_id": "BIBREF56"
},
{
"start": 689,
"end": 712,
"text": "(Habernal et al., 2013)",
"ref_id": "BIBREF26"
},
{
"start": 749,
"end": 772,
"text": "(Habernal et al., 2013)",
"ref_id": "BIBREF26"
},
{
"start": 808,
"end": 831,
"text": "(Habernal et al., 2013)",
"ref_id": "BIBREF26"
},
{
"start": 868,
"end": 891,
"text": "(Habernal et al., 2013)",
"ref_id": "BIBREF26"
}
],
"ref_spans": [
{
"start": 118,
"end": 126,
"text": "Figure 4",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "Conclusions and Further Works",
"sec_num": "6"
}
],
"back_matter": [
{
"text": "The work was partially supported by the Department of Artificial Intelligence at Wroclaw University of Science and Technology, and by European Regional Development Fund (ERDF) in RPO WD 2014-2020 (project no. RPDS.01.02.02-02-0065/20). We want to thank Miko\u0142aj Morzy for an initial review and feedback. We want to thank our annotators team -Barbara Or\u0142owska, Daria Sza\u0142amacha, Konrad Gajewski and Pawe\u0142 Odrow\u0105\u017c-Sypniewski.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgements",
"sec_num": null
},
{
"text": "A.1 Hardware and SoftwareWe performed our experiments using Python 3.9 and PyTorch (1.8.1) (and Tensorflow (2.3.0) for original mUSE). Our experimental setup consists of Intel(R) Xeon(R) CPU E5-2630 v4 @ 2.20GHz and Nvidia Tesla V100 16GB.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "A Appendices",
"sec_num": null
},
{
"text": "We present detailed lists of datasets included in our research in Tables 5 and 6 . They include language, category, dataset size, class balance and basic texts characteristics.",
"cite_spans": [],
"ref_spans": [
{
"start": 66,
"end": 80,
"text": "Tables 5 and 6",
"ref_id": null
}
],
"eq_spans": [],
"section": "A.2 Detailed Datasets Information",
"sec_num": null
},
{
"text": "We include full results of our experiments with results for each language in Figure 5 . Part with finetuning results was presented earlier in Figure 2 .",
"cite_spans": [],
"ref_spans": [
{
"start": 77,
"end": 85,
"text": "Figure 5",
"ref_id": null
},
{
"start": 142,
"end": 150,
"text": "Figure 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "A.3 Full Results for Languages",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Sentiment classifier: Logistic regression for arabic services' reviews in lebanon",
"authors": [
{
"first": "Moustafa",
"middle": [],
"last": "Marwan Al Omari",
"suffix": ""
},
{
"first": "Nacereddine",
"middle": [],
"last": "Al-Hajj",
"suffix": ""
},
{
"first": "Amani",
"middle": [],
"last": "Hammami",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Sabra",
"suffix": ""
}
],
"year": 2019,
"venue": "2019 International Conference on Computer and Information Sciences (ICCIS)",
"volume": "",
"issue": "",
"pages": "1--5",
"other_ids": {
"DOI": [
"10.1109/ICCISci.2019.8716394"
]
},
"num": null,
"urls": [],
"raw_text": "Marwan Al Omari, Moustafa Al-Hajj, Nacereddine Hammami, and Amani Sabra. 2019. Sentiment classi- fier: Logistic regression for arabic services' reviews in lebanon. In 2019 International Conference on Computer and Information Sciences (ICCIS), pages 1-5.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "LABR: A large scale Arabic book reviews dataset",
"authors": [
{
"first": "Mohamed",
"middle": [],
"last": "Aly",
"suffix": ""
},
{
"first": "Amir",
"middle": [],
"last": "Atiya",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics",
"volume": "2",
"issue": "",
"pages": "494--498",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mohamed Aly and Amir Atiya. 2013. LABR: A large scale Arabic book reviews dataset. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 494-498, Sofia, Bulgaria. Association for Com- putational Linguistics.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Representations and architectures in neural sentiment analysis for morphologically rich languages: A case study from Modern Hebrew",
"authors": [
{
"first": "Adam",
"middle": [],
"last": "Amram",
"suffix": ""
},
{
"first": "Anat",
"middle": [],
"last": "Ben David",
"suffix": ""
},
{
"first": "Reut",
"middle": [],
"last": "Tsarfaty",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 27th International Conference on Computational Linguistics",
"volume": "",
"issue": "",
"pages": "2242--2252",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Adam Amram, Anat Ben David, and Reut Tsarfaty. 2018. Representations and architectures in neu- ral sentiment analysis for morphologically rich lan- guages: A case study from Modern Hebrew. In Pro- ceedings of the 27th International Conference on Computational Linguistics, pages 2242-2252, Santa Fe, New Mexico, USA. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond",
"authors": [
{
"first": "Mikel",
"middle": [],
"last": "Artetxe",
"suffix": ""
},
{
"first": "Holger",
"middle": [],
"last": "Schwenk",
"suffix": ""
}
],
"year": 2019,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "7",
"issue": "",
"pages": "597--610",
"other_ids": {
"DOI": [
"10.1162/tacl_a_00288"
]
},
"num": null,
"urls": [],
"raw_text": "Mikel Artetxe and Holger Schwenk. 2019. Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond. Transactions of the Association for Computational Linguistics, 7:597- 610.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Multilingual multi-class sentiment classification using convolutional neural networks",
"authors": [
{
"first": "Mohammed",
"middle": [],
"last": "Attia",
"suffix": ""
},
{
"first": "Younes",
"middle": [],
"last": "Samih",
"suffix": ""
},
{
"first": "Ali",
"middle": [],
"last": "Elkahky",
"suffix": ""
},
{
"first": "Laura",
"middle": [],
"last": "Kallmeyer",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mohammed Attia, Younes Samih, Ali Elkahky, and Laura Kallmeyer. 2018. Multilingual multi-class sen- timent classification using convolutional neural net- works. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets",
"authors": [
{
"first": "Ramy",
"middle": [],
"last": "Baly",
"suffix": ""
},
{
"first": "Alaa",
"middle": [],
"last": "Khaddaj",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Hazem",
"suffix": ""
},
{
"first": "Wassim",
"middle": [],
"last": "Hajj",
"suffix": ""
},
{
"first": "Khaled",
"middle": [],
"last": "El-Hajj",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Bashir Shaban",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ramy Baly, Alaa Khaddaj, Hazem M. Hajj, Wassim El-Hajj, and Khaled Bashir Shaban. 2018. ArSentD- LEV: A Multi-Topic Corpus for Target-based Senti- ment Analysis in Arabic Levantine Tweets. In Pro- ceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Paris, France. European Language Resources Associ- ation (ELRA).",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Overview of the Evalita 2016 SENTIment POLarity Classification Task",
"authors": [
{
"first": "Francesco",
"middle": [],
"last": "Barbieri",
"suffix": ""
},
{
"first": "Valerio",
"middle": [],
"last": "Basile",
"suffix": ""
},
{
"first": "Danilo",
"middle": [],
"last": "Croce",
"suffix": ""
},
{
"first": "Malvina",
"middle": [],
"last": "Nissim",
"suffix": ""
},
{
"first": "Nicole",
"middle": [],
"last": "Novielli",
"suffix": ""
},
{
"first": "Viviana",
"middle": [],
"last": "Patti",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) & Fifth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Francesco Barbieri, Valerio Basile, Danilo Croce, Malv- ina Nissim, Nicole Novielli, and Viviana Patti. 2016. Overview of the Evalita 2016 SENTIment POLarity Classification Task. In Proceedings of Third Italian Conference on Computational Linguistics (CLiC-it 2016) & Fifth Evaluation Campaign of Natural Lan- guage Processing and Speech Tools for Italian. Final Workshop (EVALITA 2016), Naples, Italy.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Author's sentiment prediction",
"authors": [
{
"first": "Mohaddeseh",
"middle": [],
"last": "Bastan",
"suffix": ""
},
{
"first": "Mahnaz",
"middle": [],
"last": "Koupaee",
"suffix": ""
},
{
"first": "Youngseo",
"middle": [],
"last": "Son",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Sicoli",
"suffix": ""
},
{
"first": "Niranjan",
"middle": [],
"last": "Balasubramanian",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 28th International Conference on Computational Linguistics",
"volume": "",
"issue": "",
"pages": "604--615",
"other_ids": {
"DOI": [
"10.18653/v1/2020.coling-main.52"
]
},
"num": null,
"urls": [],
"raw_text": "Mohaddeseh Bastan, Mahnaz Koupaee, Youngseo Son, Richard Sicoli, and Niranjan Balasubramanian. 2020. Author's sentiment prediction. In Proceedings of the 28th International Conference on Computational Lin- guistics, pages 604-615, Barcelona, Spain (Online). International Committee on Computational Linguis- tics.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Reliable baselines for sentiment analysis in resource-limited languages: The Serbian movie review dataset",
"authors": [
{
"first": "Vuk",
"middle": [],
"last": "Batanovi\u0107",
"suffix": ""
},
{
"first": "Bo\u0161ko",
"middle": [],
"last": "Nikoli\u0107",
"suffix": ""
},
{
"first": "Milan",
"middle": [],
"last": "Milosavljevi\u0107",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)",
"volume": "",
"issue": "",
"pages": "2688--2696",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Vuk Batanovi\u0107, Bo\u0161ko Nikoli\u0107, and Milan Milosavlje- vi\u0107. 2016. Reliable baselines for sentiment analysis in resource-limited languages: The Serbian movie review dataset. In Proceedings of the Tenth Inter- national Conference on Language Resources and Evaluation (LREC'16), pages 2688-2696, Portoro\u017e, Slovenia. European Language Resources Association (ELRA).",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts",
"authors": [
{
"first": "Vuk",
"middle": [],
"last": "Batanovi\u0107",
"suffix": ""
},
{
"first": "Milo\u0161",
"middle": [],
"last": "Cvetanovi\u0107",
"suffix": ""
},
{
"first": "Bo\u0161ko",
"middle": [],
"last": "Nikoli\u0107",
"suffix": ""
}
],
"year": 2020,
"venue": "PLOS ONE",
"volume": "15",
"issue": "11",
"pages": "1--30",
"other_ids": {
"DOI": [
"10.1371/journal.pone.0242050"
]
},
"num": null,
"urls": [],
"raw_text": "Vuk Batanovi\u0107, Milo\u0161 Cvetanovi\u0107, and Bo\u0161ko Nikoli\u0107. 2020. A versatile framework for resource-limited sentiment articulation, annotation, and analysis of short texts. PLOS ONE, 15(11):1-30.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Building a sentiment corpus of tweets in Brazilian Portuguese",
"authors": [
{
"first": "Henrico",
"middle": [],
"last": "Brum",
"suffix": ""
},
{
"first": "Maria",
"middle": [],
"last": "Das Gra\u00e7as Volpe",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Nunes",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Henrico Brum and Maria das Gra\u00e7as Volpe Nunes. 2018. Building a sentiment corpus of tweets in Brazilian Portuguese. In Proceedings of the Eleventh Inter- national Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Annotated news corpora and a lexicon for sentiment analysis in slovene",
"authors": [
{
"first": "Jo\u017ee",
"middle": [],
"last": "Bu\u010dar",
"suffix": ""
},
{
"first": "Martin",
"middle": [],
"last": "\u017dnidar\u0161i\u010d",
"suffix": ""
},
{
"first": "Janez",
"middle": [],
"last": "Povh",
"suffix": ""
}
],
"year": 2018,
"venue": "Language Resources and Evaluation",
"volume": "52",
"issue": "3",
"pages": "895--919",
"other_ids": {
"DOI": [
"10.1007/s10579-018-9413-3"
]
},
"num": null,
"urls": [],
"raw_text": "Jo\u017ee Bu\u010dar, Martin \u017dnidar\u0161i\u010d, and Janez Povh. 2018. Annotated news corpora and a lexicon for sentiment analysis in slovene. Language Resources and Evalu- ation, 52(3):895-919.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Multilingual sentiment analysis: An RNN-based framework for limited data",
"authors": [
{
"first": "F",
"middle": [],
"last": "Ethem",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Can",
"suffix": ""
}
],
"year": 2018,
"venue": "Computing Research Repository",
"volume": "1",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1806.04511"
]
},
"num": null,
"urls": [],
"raw_text": "Ethem F. Can, Aysu Ezen-Can, and Fazli Can. 2018. Multilingual sentiment analysis: An RNN-based framework for limited data. Computing Research Repository, arXiv:1806.04511. Version 1.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Hierarchical pre-training for sequence labelling in spoken dialog",
"authors": [
{
"first": "Emile",
"middle": [],
"last": "Chapuis",
"suffix": ""
},
{
"first": "Pierre",
"middle": [],
"last": "Colombo",
"suffix": ""
},
{
"first": "Matteo",
"middle": [],
"last": "Manica",
"suffix": ""
},
{
"first": "Matthieu",
"middle": [],
"last": "Labeau",
"suffix": ""
},
{
"first": "Chlo\u00e9",
"middle": [],
"last": "Clavel",
"suffix": ""
}
],
"year": 2020,
"venue": "Findings of the Association for Computational Linguistics: EMNLP 2020",
"volume": "",
"issue": "",
"pages": "2636--2648",
"other_ids": {
"DOI": [
"10.18653/v1/2020.findings-emnlp.239"
]
},
"num": null,
"urls": [],
"raw_text": "Emile Chapuis, Pierre Colombo, Matteo Manica, Matthieu Labeau, and Chlo\u00e9 Clavel. 2020. Hier- archical pre-training for sequence labelling in spoken dialog. In Findings of the Association for Computa- tional Linguistics: EMNLP 2020, pages 2636-2648, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "A Twitter corpus and benchmark resources for German sentiment analysis",
"authors": [
{
"first": "Mark",
"middle": [],
"last": "Cieliebak",
"suffix": ""
},
{
"first": "Jan",
"middle": [
"Milan"
],
"last": "Deriu",
"suffix": ""
},
{
"first": "Dominic",
"middle": [],
"last": "Egger",
"suffix": ""
},
{
"first": "Fatih",
"middle": [],
"last": "Uzdilli",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the Fifth International Workshop on Natural Language Processing for Social Media",
"volume": "",
"issue": "",
"pages": "45--51",
"other_ids": {
"DOI": [
"10.18653/v1/W17-1106"
]
},
"num": null,
"urls": [],
"raw_text": "Mark Cieliebak, Jan Milan Deriu, Dominic Egger, and Fatih Uzdilli. 2017. A Twitter corpus and benchmark resources for German sentiment analysis. In Proceed- ings of the Fifth International Workshop on Natural Language Processing for Social Media, pages 45- 51, Valencia, Spain. Association for Computational Linguistics.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Unsupervised cross-lingual representation learning at scale",
"authors": [
{
"first": "Edouard",
"middle": [],
"last": "Guzm\u00e1n",
"suffix": ""
},
{
"first": "Myle",
"middle": [],
"last": "Grave",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Ott",
"suffix": ""
},
{
"first": "Veselin",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Stoyanov",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "8440--8451",
"other_ids": {
"DOI": [
"10.18653/v1/2020.acl-main.747"
]
},
"num": null,
"urls": [],
"raw_text": "Guzm\u00e1n, Edouard Grave, Myle Ott, Luke Zettle- moyer, and Veselin Stoyanov. 2020. Unsupervised cross-lingual representation learning at scale. In Pro- ceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 8440- 8451, Online. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Crosslingual language model pretraining",
"authors": [
{
"first": "Alexis",
"middle": [],
"last": "Conneau",
"suffix": ""
},
{
"first": "Guillaume",
"middle": [],
"last": "Lample",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 33rd International Conference on Neural Information Processing Systems",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"https://dl.acm.org/doi/10.5555/3454287.3454921"
]
},
"num": null,
"urls": [],
"raw_text": "Alexis Conneau and Guillaume Lample. 2019. Cross- lingual language model pretraining. In Proceedings of the 33rd International Conference on Neural In- formation Processing Systems, Red Hook, NY, USA. Curran Associates Inc.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "XNLI: Evaluating crosslingual sentence representations",
"authors": [
{
"first": "Alexis",
"middle": [],
"last": "Conneau",
"suffix": ""
},
{
"first": "Ruty",
"middle": [],
"last": "Rinott",
"suffix": ""
},
{
"first": "Guillaume",
"middle": [],
"last": "Lample",
"suffix": ""
},
{
"first": "Adina",
"middle": [],
"last": "Williams",
"suffix": ""
},
{
"first": "Samuel",
"middle": [],
"last": "Bowman",
"suffix": ""
},
{
"first": "Holger",
"middle": [],
"last": "Schwenk",
"suffix": ""
},
{
"first": "Veselin",
"middle": [],
"last": "Stoyanov",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2475--2485",
"other_ids": {
"DOI": [
"10.18653/v1/D18-1269"
]
},
"num": null,
"urls": [],
"raw_text": "Alexis Conneau, Ruty Rinott, Guillaume Lample, Adina Williams, Samuel Bowman, Holger Schwenk, and Veselin Stoyanov. 2018. XNLI: Evaluating cross- lingual sentence representations. In Proceedings of the 2018 Conference on Empirical Methods in Nat- ural Language Processing, pages 2475-2485, Brus- sels, Belgium. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Experiments in sentiment classification of movie reviews in spanish",
"authors": [
{
"first": "L",
"middle": [],
"last": "Fermin",
"suffix": ""
},
{
"first": "Jose",
"middle": [
"A"
],
"last": "Cruz",
"suffix": ""
},
{
"first": "Fernando",
"middle": [],
"last": "Troyano",
"suffix": ""
},
{
"first": "Javier",
"middle": [],
"last": "Enriquez",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ortega",
"suffix": ""
}
],
"year": 2008,
"venue": "Procesamiento del Lenguaje Natural",
"volume": "41",
"issue": "",
"pages": "73--80",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Fermin L Cruz, Jose A Troyano, Fernando Enriquez, and Javier Ortega. 2008. Experiments in sentiment classification of movie reviews in spanish. Proce- samiento del Lenguaje Natural, 41:73-80.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Multilingual sentiment analysis: state of the art and independent comparison of techniques",
"authors": [
{
"first": "Kia",
"middle": [],
"last": "Dashtipour",
"suffix": ""
},
{
"first": "Soujanya",
"middle": [],
"last": "Poria",
"suffix": ""
},
{
"first": "Amir",
"middle": [],
"last": "Hussain",
"suffix": ""
},
{
"first": "Erik",
"middle": [],
"last": "Cambria",
"suffix": ""
},
{
"first": "Y",
"middle": [
"A"
],
"last": "Ahmad",
"suffix": ""
},
{
"first": "Alexander",
"middle": [],
"last": "Hawalah",
"suffix": ""
},
{
"first": "Qiang",
"middle": [],
"last": "Gelbukh",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Zhou",
"suffix": ""
}
],
"year": 2016,
"venue": "Cognitive computation",
"volume": "8",
"issue": "4",
"pages": "757--771",
"other_ids": {
"DOI": [
"10.1007/s12559-016-9415-7"
]
},
"num": null,
"urls": [],
"raw_text": "Kia Dashtipour, Soujanya Poria, Amir Hussain, Erik Cambria, Ahmad YA Hawalah, Alexander Gelbukh, and Qiang Zhou. 2016. Multilingual sentiment anal- ysis: state of the art and independent comparison of techniques. Cognitive computation, 8(4):757-771.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Statistical comparisons of classifiers over multiple data sets",
"authors": [
{
"first": "Janez",
"middle": [],
"last": "Dem\u0161ar",
"suffix": ""
}
],
"year": 2006,
"venue": "Journal of Machine Learning Research",
"volume": "7",
"issue": "",
"pages": "1--30",
"other_ids": {
"DOI": [
"https://dl.acm.org/doi/10.5555/1248547.1248548"
]
},
"num": null,
"urls": [],
"raw_text": "Janez Dem\u0161ar. 2006. Statistical comparisons of clas- sifiers over multiple data sets. Journal of Machine Learning Research, 7:1-30.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "BERT: Pre-training of deep bidirectional transformers for language understanding",
"authors": [
{
"first": "Jacob",
"middle": [],
"last": "Devlin",
"suffix": ""
},
{
"first": "Ming-Wei",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "Kenton",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Kristina",
"middle": [],
"last": "Toutanova",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "4171--4186",
"other_ids": {
"DOI": [
"10.18653/v1/N19-1423"
]
},
"num": null,
"urls": [],
"raw_text": "Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "BRAD 1.0: Book reviews in arabic dataset",
"authors": [
{
"first": "Ashraf",
"middle": [],
"last": "Elnagar",
"suffix": ""
},
{
"first": "Omar",
"middle": [],
"last": "Einea",
"suffix": ""
}
],
"year": 2016,
"venue": "2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA)",
"volume": "",
"issue": "",
"pages": "1--8",
"other_ids": {
"DOI": [
"10.1109/AICCSA.2016.7945800"
]
},
"num": null,
"urls": [],
"raw_text": "Ashraf Elnagar and Omar Einea. 2016. BRAD 1.0: Book reviews in arabic dataset. In 2016 IEEE/ACS 13th International Conference of Computer Systems and Applications (AICCSA), pages 1-8.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications",
"authors": [
{
"first": "Ashraf",
"middle": [],
"last": "Elnagar",
"suffix": ""
},
{
"first": "Yasmin",
"middle": [
"S"
],
"last": "Khalifa",
"suffix": ""
},
{
"first": "Anas",
"middle": [],
"last": "Einea",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.1007/978-3-319-67056-0_3"
]
},
"num": null,
"urls": [],
"raw_text": "Ashraf Elnagar, Yasmin S. Khalifa, and Anas Einea. 2018. Hotel Arabic-Reviews Dataset Construction for Sentiment Analysis Applications. Springer Inter- national Publishing, Cham.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Language-agnostic BERT Sentence Embedding",
"authors": [
{
"first": "Fangxiaoyu",
"middle": [],
"last": "Feng",
"suffix": ""
},
{
"first": "Yinfei",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Daniel",
"middle": [],
"last": "Cer",
"suffix": ""
},
{
"first": "Naveen",
"middle": [],
"last": "Arivazhagan",
"suffix": ""
},
{
"first": "Wei",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2020,
"venue": "Computing Research Repository",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:2007.01852"
]
},
"num": null,
"urls": [],
"raw_text": "Fangxiaoyu Feng, Yinfei Yang, Daniel Cer, Naveen Ari- vazhagan, and Wei Wang. 2020. Language-agnostic BERT Sentence Embedding. Computing Research Repository, arXiv:2007.01852. Version 2.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Sentiment analysis in Czech social media using supervised machine learning",
"authors": [
{
"first": "Ivan",
"middle": [],
"last": "Habernal",
"suffix": ""
},
{
"first": "Tom\u00e1\u0161",
"middle": [],
"last": "Pt\u00e1\u010dek",
"suffix": ""
},
{
"first": "Josef",
"middle": [],
"last": "Steinberger",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis",
"volume": "",
"issue": "",
"pages": "65--74",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ivan Habernal, Tom\u00e1\u0161 Pt\u00e1\u010dek, and Josef Steinberger. 2013. Sentiment analysis in Czech social media us- ing supervised machine learning. In Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analy- sis, pages 65-74, Atlanta, Georgia. Association for Computational Linguistics.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Mansoureh Anvari, and Seyed Abolghasem Mirroshandel",
"authors": [
{
"first": "Pedram",
"middle": [],
"last": "Hosseini",
"suffix": ""
},
{
"first": "Ali",
"middle": [
"Ahmadian"
],
"last": "Ramaki",
"suffix": ""
},
{
"first": "Hassan",
"middle": [],
"last": "Maleki",
"suffix": ""
}
],
"year": 2018,
"venue": "Computing Research Repository",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1801.07737"
]
},
"num": null,
"urls": [],
"raw_text": "Pedram Hosseini, Ali Ahmadian Ramaki, Hassan Maleki, Mansoureh Anvari, and Seyed Abolghasem Mirroshandel. 2018. SentiPers: A sentiment analysis corpus for persian. Computing Research Repository, arXiv:1801.07737. Version 2.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "XTREME: A massively multilingual multitask benchmark for evaluating cross-lingual generalisation",
"authors": [
{
"first": "Junjie",
"middle": [],
"last": "Hu",
"suffix": ""
},
{
"first": "Sebastian",
"middle": [],
"last": "Ruder",
"suffix": ""
},
{
"first": "Aditya",
"middle": [],
"last": "Siddhant",
"suffix": ""
},
{
"first": "Graham",
"middle": [],
"last": "Neubig",
"suffix": ""
},
{
"first": "Orhan",
"middle": [],
"last": "Firat",
"suffix": ""
},
{
"first": "Melvin",
"middle": [],
"last": "Johnson",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 37th International Conference on Machine Learning",
"volume": "119",
"issue": "",
"pages": "4411--4421",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Junjie Hu, Sebastian Ruder, Aditya Siddhant, Gra- ham Neubig, Orhan Firat, and Melvin Johnson. 2020. XTREME: A massively multilingual multi- task benchmark for evaluating cross-lingual gener- alisation. In Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 4411-4421. PMLR.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "VADER: A parsimonious rule-based model for sentiment analysis of social media text",
"authors": [
{
"first": "J",
"middle": [],
"last": "Clayton",
"suffix": ""
},
{
"first": "Eric",
"middle": [],
"last": "Hutto",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Gilbert",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the International AAAI Conference on Web and Social Media",
"volume": "8",
"issue": "",
"pages": "216--225",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Clayton J. Hutto and Eric Gilbert. 2014. VADER: A par- simonious rule-based model for sentiment analysis of social media text. In Proceedings of the Interna- tional AAAI Conference on Web and Social Media, volume 8, pages 216-225.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Twitter us airline sentiment",
"authors": [
{
"first": "Crowdflower",
"middle": [],
"last": "Inc",
"suffix": ""
}
],
"year": 2015,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Crowdflower Inc. 2015. Twitter us airline sentiment.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Cross-lingual ability of multilingual bert: An empirical study",
"authors": [
{
"first": "K",
"middle": [],
"last": "Karthikeyan",
"suffix": ""
},
{
"first": "Zihan",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Stephen",
"middle": [],
"last": "Mayhew",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Roth",
"suffix": ""
}
],
"year": 2020,
"venue": "International Conference on Learning Representations",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Karthikeyan K, Zihan Wang, Stephen Mayhew, and Dan Roth. 2020. Cross-lingual ability of multilingual bert: An empirical study. In International Conference on Learning Representations.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Cross-lingual deep neural transfer learning in sentiment analysis",
"authors": [
{
"first": "Kamil",
"middle": [],
"last": "Kanclerz",
"suffix": ""
},
{
"first": "Piotr",
"middle": [],
"last": "Mi\u0142kowski",
"suffix": ""
}
],
"year": 2020,
"venue": "Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES2020",
"volume": "176",
"issue": "",
"pages": "128--137",
"other_ids": {
"DOI": [
"10.1016/j.procs.2020.08.014"
]
},
"num": null,
"urls": [],
"raw_text": "Kamil Kanclerz, Piotr Mi\u0142kowski, and Jan Koco\u0144. 2020. Cross-lingual deep neural transfer learning in senti- ment analysis. Procedia Computer Science, 176:128- 137. Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 24th International Conference KES2020.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Sentiment analysis and opinion mining applied to scientific paper reviews",
"authors": [
{
"first": "Brian",
"middle": [],
"last": "Keith Norambuena",
"suffix": ""
},
{
"first": "Exequiel",
"middle": [],
"last": "Lettura",
"suffix": ""
},
{
"first": "Claudio",
"middle": [],
"last": "Villegas",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "23",
"issue": "",
"pages": "191--214",
"other_ids": {
"DOI": [
"10.3233/IDA-173807"
]
},
"num": null,
"urls": [],
"raw_text": "Brian Keith Norambuena, Exequiel Lettura, and Clau- dio Villegas. 2019. Sentiment analysis and opinion mining applied to scientific paper reviews. Intelligent Data Analysis, 23:191-214.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "The multilingual Amazon reviews corpus",
"authors": [
{
"first": "Phillip",
"middle": [],
"last": "Keung",
"suffix": ""
},
{
"first": "Yichao",
"middle": [],
"last": "Lu",
"suffix": ""
},
{
"first": "Gy\u00f6rgy",
"middle": [],
"last": "Szarvas",
"suffix": ""
},
{
"first": "Noah",
"middle": [
"A"
],
"last": "Smith",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "4563--4568",
"other_ids": {
"DOI": [
"10.18653/v1/2020.emnlp-main.369"
]
},
"num": null,
"urls": [],
"raw_text": "Phillip Keung, Yichao Lu, Gy\u00f6rgy Szarvas, and Noah A. Smith. 2020. The multilingual Amazon reviews cor- pus. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4563-4568, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Multi-level sentiment analysis of PolEmo 2.0: Extended corpus of multi-domain consumer reviews",
"authors": [
{
"first": "Jan",
"middle": [],
"last": "Koco\u0144",
"suffix": ""
},
{
"first": "Piotr",
"middle": [],
"last": "Mi\u0142kowski",
"suffix": ""
},
{
"first": "Monika",
"middle": [],
"last": "Za\u015bko-Zieli\u0144ska",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)",
"volume": "",
"issue": "",
"pages": "980--991",
"other_ids": {
"DOI": [
"10.18653/v1/K19-1092"
]
},
"num": null,
"urls": [],
"raw_text": "Jan Koco\u0144, Piotr Mi\u0142kowski, and Monika Za\u015bko- Zieli\u0144ska. 2019. Multi-level sentiment analysis of PolEmo 2.0: Extended corpus of multi-domain con- sumer reviews. In Proceedings of the 23rd Confer- ence on Computational Natural Language Learning (CoNLL), pages 980-991, Hong Kong, China. Asso- ciation for Computational Linguistics.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "MLQA: Evaluating cross-lingual extractive question answering",
"authors": [
{
"first": "Patrick",
"middle": [],
"last": "Lewis",
"suffix": ""
},
{
"first": "Barlas",
"middle": [],
"last": "Oguz",
"suffix": ""
},
{
"first": "Ruty",
"middle": [],
"last": "Rinott",
"suffix": ""
},
{
"first": "Sebastian",
"middle": [],
"last": "Riedel",
"suffix": ""
},
{
"first": "Holger",
"middle": [],
"last": "Schwenk",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "7315--7330",
"other_ids": {
"DOI": [
"10.18653/v1/2020.acl-main.653"
]
},
"num": null,
"urls": [],
"raw_text": "Patrick Lewis, Barlas Oguz, Ruty Rinott, Sebastian Riedel, and Holger Schwenk. 2020. MLQA: Evalu- ating cross-lingual extractive question answering. In Proceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 7315- 7330, Online. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "An empirical study on sentiment classification of Chinese review using word embedding",
"authors": [
{
"first": "Yiou",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Hang",
"middle": [],
"last": "Lei",
"suffix": ""
},
{
"first": "Jia",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Xiaoyu",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 29th Pacific Asia Conference on Language, Information and Computation: Posters",
"volume": "",
"issue": "",
"pages": "258--266",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yiou Lin, Hang Lei, Jia Wu, and Xiaoyu Li. 2015. An empirical study on sentiment classification of Chi- nese review using word embedding. In Proceedings of the 29th Pacific Asia Conference on Language, In- formation and Computation: Posters, pages 258-266, Shanghai, China.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "Yung-Sung Chuang, and Hung yi Lee. 2020. What makes multilingual bert multilingual? Computing Research Repository",
"authors": [
{
"first": "Chi-Liang",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Tsung-Yuan",
"middle": [],
"last": "Hsu",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "1",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:2010.10938"
]
},
"num": null,
"urls": [],
"raw_text": "Chi-Liang Liu, Tsung-Yuan Hsu, Yung-Sung Chuang, and Hung yi Lee. 2020. What makes multilingual bert multilingual? Computing Research Repository, arXiv:2010.10938. Version 1.",
"links": null
},
"BIBREF39": {
"ref_id": "b39",
"title": "Good debt or bad debt: Detecting semantic orientations in economic texts",
"authors": [
{
"first": "Pekka",
"middle": [],
"last": "Malo",
"suffix": ""
},
{
"first": "Ankur",
"middle": [],
"last": "Sinha",
"suffix": ""
},
{
"first": "Pekka",
"middle": [],
"last": "Korhonen",
"suffix": ""
}
],
"year": 2014,
"venue": "Journal of the Association for Information Science and Technology",
"volume": "65",
"issue": "4",
"pages": "782--796",
"other_ids": {
"DOI": [
"10.1002/asi.23062"
]
},
"num": null,
"urls": [],
"raw_text": "Pekka Malo, Ankur Sinha, Pekka Korhonen, Jyrki Wal- lenius, and Pyry Takala. 2014. Good debt or bad debt: Detecting semantic orientations in economic texts. Journal of the Association for Information Science and Technology, 65(4):782-796.",
"links": null
},
"BIBREF40": {
"ref_id": "b40",
"title": "Efficient estimation of word representations in vector space",
"authors": [
{
"first": "Tom\u00e1s",
"middle": [],
"last": "Mikolov",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Greg",
"middle": [],
"last": "Corrado",
"suffix": ""
},
{
"first": "Jeffrey",
"middle": [],
"last": "Dean",
"suffix": ""
}
],
"year": 2013,
"venue": "1st International Conference on Learning Representations",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tom\u00e1s Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013a. Efficient estimation of word representa- tions in vector space. In 1st International Conference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Pro- ceedings.",
"links": null
},
"BIBREF41": {
"ref_id": "b41",
"title": "Exploiting similarities among languages for machine translation",
"authors": [
{
"first": "Tomas",
"middle": [],
"last": "Mikolov",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Quoc",
"suffix": ""
},
{
"first": "Ilya",
"middle": [],
"last": "Le",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Sutskever",
"suffix": ""
}
],
"year": 2013,
"venue": "Computing Research Repository",
"volume": "1",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1309.4168"
]
},
"num": null,
"urls": [],
"raw_text": "Tomas Mikolov, Quoc V. Le, and Ilya Sutskever. 2013b. Exploiting similarities among languages for ma- chine translation. Computing Research Repository, arXiv:1309.4168. Version 1.",
"links": null
},
"BIBREF42": {
"ref_id": "b42",
"title": "Multilingual twitter sentiment classification: The role of human annotators",
"authors": [
{
"first": "Igor",
"middle": [],
"last": "Mozeti\u010d",
"suffix": ""
},
{
"first": "Miha",
"middle": [],
"last": "Gr\u010dar",
"suffix": ""
},
{
"first": "Jasmina",
"middle": [],
"last": "Smailovi\u0107",
"suffix": ""
}
],
"year": 2016,
"venue": "PLOS ONE",
"volume": "11",
"issue": "5",
"pages": "1--26",
"other_ids": {
"DOI": [
"10.1371/journal.pone.0155036"
]
},
"num": null,
"urls": [],
"raw_text": "Igor Mozeti\u010d, Miha Gr\u010dar, and Jasmina Smailovi\u0107. 2016. Multilingual twitter sentiment classification: The role of human annotators. PLOS ONE, 11(5):1-26.",
"links": null
},
"BIBREF43": {
"ref_id": "b43",
"title": "ASTD: Arabic sentiment tweets dataset",
"authors": [
{
"first": "Mahmoud",
"middle": [],
"last": "Nabil",
"suffix": ""
},
{
"first": "Mohamed",
"middle": [],
"last": "Aly",
"suffix": ""
},
{
"first": "Amir",
"middle": [],
"last": "Atiya",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2515--2519",
"other_ids": {
"DOI": [
"10.18653/v1/D15-1299"
]
},
"num": null,
"urls": [],
"raw_text": "Mahmoud Nabil, Mohamed Aly, and Amir Atiya. 2015. ASTD: Arabic sentiment tweets dataset. In Proceed- ings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2515-2519, Lisbon, Portugal. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF44": {
"ref_id": "b44",
"title": "Language-independent twitter sentiment analysis",
"authors": [
{
"first": "Sascha",
"middle": [],
"last": "Narr",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "H\u00fclfenhaus",
"suffix": ""
},
{
"first": "Sahin",
"middle": [],
"last": "Albayrak",
"suffix": ""
}
],
"year": 2012,
"venue": "Workshop on Knowledge Discovery, Data Mining and Machine Learning",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sascha Narr, Michael H\u00fclfenhaus, and Sahin Albayrak. 2012. Language-independent twitter sentiment anal- ysis. In Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML-2012).",
"links": null
},
"BIBREF45": {
"ref_id": "b45",
"title": "Distribution-free Multiple Comparisons",
"authors": [
{
"first": "Peter",
"middle": [],
"last": "Nemenyi",
"suffix": ""
}
],
"year": 1963,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Peter Nemenyi. 1963. Distribution-free Multiple Com- parisons. Princeton University.",
"links": null
},
"BIBREF46": {
"ref_id": "b46",
"title": "Justifying recommendations using distantly-labeled reviews and fine-grained aspects",
"authors": [
{
"first": "Jianmo",
"middle": [],
"last": "Ni",
"suffix": ""
},
{
"first": "Jiacheng",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Julian",
"middle": [],
"last": "Mcauley",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
"volume": "",
"issue": "",
"pages": "188--197",
"other_ids": {
"DOI": [
"10.18653/v1/D19-1018"
]
},
"num": null,
"urls": [],
"raw_text": "Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th Interna- tional Joint Conference on Natural Language Pro- cessing (EMNLP-IJCNLP), pages 188-197, Hong Kong, China. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF47": {
"ref_id": "b47",
"title": "Confident learning: Estimating uncertainty in dataset labels",
"authors": [
{
"first": "Curtis",
"middle": [],
"last": "Northcutt",
"suffix": ""
},
{
"first": "Lu",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "Isaac",
"middle": [],
"last": "Chuang",
"suffix": ""
}
],
"year": 2021,
"venue": "Journal of Artificial Intelligence Research",
"volume": "70",
"issue": "",
"pages": "1373--1411",
"other_ids": {
"DOI": [
"10.1613/jair.1.12125"
]
},
"num": null,
"urls": [],
"raw_text": "Curtis Northcutt, Lu Jiang, and Isaac Chuang. 2021. Confident learning: Estimating uncertainty in dataset labels. Journal of Artificial Intelligence Research, 70:1373-1411.",
"links": null
},
"BIBREF48": {
"ref_id": "b48",
"title": "SemEval-2020 task 9: Overview of sentiment analysis of code-mixed tweets",
"authors": [
{
"first": "Parth",
"middle": [],
"last": "Patwa",
"suffix": ""
},
{
"first": "Gustavo",
"middle": [],
"last": "Aguilar",
"suffix": ""
},
{
"first": "Sudipta",
"middle": [],
"last": "Kar",
"suffix": ""
},
{
"first": "Suraj",
"middle": [],
"last": "Pandey",
"suffix": ""
},
{
"first": "Pykl",
"middle": [],
"last": "Srinivas",
"suffix": ""
},
{
"first": "Bj\u00f6rn",
"middle": [],
"last": "Gamb\u00e4ck",
"suffix": ""
},
{
"first": "Tanmoy",
"middle": [],
"last": "Chakraborty",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the Fourteenth Workshop on Semantic Evaluation",
"volume": "",
"issue": "",
"pages": "774--790",
"other_ids": {
"DOI": [
"10.18653/v1/2020.semeval-1.100"
]
},
"num": null,
"urls": [],
"raw_text": "Parth Patwa, Gustavo Aguilar, Sudipta Kar, Suraj Pandey, Srinivas PYKL, Bj\u00f6rn Gamb\u00e4ck, Tanmoy Chakraborty, Thamar Solorio, and Amitava Das. 2020. SemEval-2020 task 9: Overview of sentiment analysis of code-mixed tweets. In Proceedings of the Fourteenth Workshop on Semantic Evaluation, pages 774-790, Barcelona (online). International Commit- tee for Computational Linguistics.",
"links": null
},
"BIBREF49": {
"ref_id": "b49",
"title": "Zero-shot learning for cross-lingual news sentiment classification",
"authors": [
{
"first": "Andra\u017e",
"middle": [],
"last": "Pelicon",
"suffix": ""
},
{
"first": "Marko",
"middle": [],
"last": "Pranji\u0107",
"suffix": ""
},
{
"first": "Dragana",
"middle": [],
"last": "Miljkovi\u0107",
"suffix": ""
},
{
"first": "Bla\u017e",
"middle": [],
"last": "\u0160krlj",
"suffix": ""
},
{
"first": "Senja",
"middle": [],
"last": "Pollak",
"suffix": ""
}
],
"year": 2020,
"venue": "Applied Sciences",
"volume": "",
"issue": "17",
"pages": "",
"other_ids": {
"DOI": [
"10.3390/app10175993"
]
},
"num": null,
"urls": [],
"raw_text": "Andra\u017e Pelicon, Marko Pranji\u0107, Dragana Miljkovi\u0107, Bla\u017e \u0160krlj, and Senja Pollak. 2020. Zero-shot learn- ing for cross-lingual news sentiment classification. Applied Sciences, 10(17).",
"links": null
},
"BIBREF50": {
"ref_id": "b50",
"title": "Making monolingual sentence embeddings multilingual using knowledge distillation",
"authors": [
{
"first": "Nils",
"middle": [],
"last": "Reimers",
"suffix": ""
},
{
"first": "Iryna",
"middle": [],
"last": "Gurevych",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "4512--4525",
"other_ids": {
"DOI": [
"10.18653/v1/2020.emnlp-main.365"
]
},
"num": null,
"urls": [],
"raw_text": "Nils Reimers and Iryna Gurevych. 2020. Making monolingual sentence embeddings multilingual us- ing knowledge distillation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 4512-4525, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF51": {
"ref_id": "b51",
"title": "RuSentiment: An enriched sentiment analysis dataset for social media in Russian",
"authors": [
{
"first": "Anna",
"middle": [],
"last": "Rogers",
"suffix": ""
},
{
"first": "Alexey",
"middle": [],
"last": "Romanov",
"suffix": ""
},
{
"first": "Anna",
"middle": [],
"last": "Rumshisky",
"suffix": ""
},
{
"first": "Svitlana",
"middle": [],
"last": "Volkova",
"suffix": ""
},
{
"first": "Mikhail",
"middle": [],
"last": "Gronas",
"suffix": ""
},
{
"first": "Alex",
"middle": [],
"last": "Gribov",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 27th International Conference on Computational Linguistics",
"volume": "",
"issue": "",
"pages": "755--763",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Anna Rogers, Alexey Romanov, Anna Rumshisky, Svit- lana Volkova, Mikhail Gronas, and Alex Gribov. 2018. RuSentiment: An enriched sentiment analysis dataset for social media in Russian. In Proceedings of the 27th International Conference on Computational Linguistics, pages 755-763, Santa Fe, New Mexico, USA. Association for Computational Linguistics.",
"links": null
},
"BIBREF52": {
"ref_id": "b52",
"title": "SemEval-2017 task 4: Sentiment analysis in Twitter",
"authors": [
{
"first": "Sara",
"middle": [],
"last": "Rosenthal",
"suffix": ""
},
{
"first": "Noura",
"middle": [],
"last": "Farra",
"suffix": ""
},
{
"first": "Preslav",
"middle": [],
"last": "Nakov",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)",
"volume": "",
"issue": "",
"pages": "502--518",
"other_ids": {
"DOI": [
"10.18653/v1/S17-2088"
]
},
"num": null,
"urls": [],
"raw_text": "Sara Rosenthal, Noura Farra, and Preslav Nakov. 2017. SemEval-2017 task 4: Sentiment analysis in Twitter. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 502- 518, Vancouver, Canada. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF53": {
"ref_id": "b53",
"title": "A survey of cross-lingual word embedding models",
"authors": [
{
"first": "Sebastian",
"middle": [],
"last": "Ruder",
"suffix": ""
},
{
"first": "Ivan",
"middle": [],
"last": "Vuli\u0107",
"suffix": ""
},
{
"first": "Anders",
"middle": [],
"last": "S\u00f8gaard",
"suffix": ""
}
],
"year": 2019,
"venue": "Journal of Artificial Intelligence Research",
"volume": "65",
"issue": "",
"pages": "569--631",
"other_ids": {
"DOI": [
"10.1613/jair.1.11640"
]
},
"num": null,
"urls": [],
"raw_text": "Sebastian Ruder, Ivan Vuli\u0107, and Anders S\u00f8gaard. 2019. A survey of cross-lingual word embedding mod- els. Journal of Artificial Intelligence Research, 65:569-631.",
"links": null
},
"BIBREF54": {
"ref_id": "b54",
"title": "KLEJ: Comprehensive benchmark for Polish language understanding",
"authors": [
{
"first": "Piotr",
"middle": [],
"last": "Rybak",
"suffix": ""
},
{
"first": "Robert",
"middle": [],
"last": "Mroczkowski",
"suffix": ""
},
{
"first": "Janusz",
"middle": [],
"last": "Tracz",
"suffix": ""
},
{
"first": "Ireneusz",
"middle": [],
"last": "Gawlik",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "1191--1201",
"other_ids": {
"DOI": [
"10.18653/v1/2020.acl-main.111"
]
},
"num": null,
"urls": [],
"raw_text": "Piotr Rybak, Robert Mroczkowski, Janusz Tracz, and Ireneusz Gawlik. 2020. KLEJ: Comprehensive benchmark for Polish language understanding. In Proceedings of the 58th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 1191- 1201, Online. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF55": {
"ref_id": "b55",
"title": "A review on multi-lingual sentiment analysis by machine learning methods",
"authors": [
{
"first": "Santwana",
"middle": [],
"last": "Sagnika",
"suffix": ""
},
{
"first": "Anshuman",
"middle": [],
"last": "Pattanaik",
"suffix": ""
},
{
"first": "Bhabani",
"middle": [],
"last": "Shankar Prasad Mishra",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Saroj",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Meher",
"suffix": ""
}
],
"year": 2020,
"venue": "Journal of Engineering Science & Technology Review",
"volume": "13",
"issue": "2",
"pages": "154--166",
"other_ids": {
"DOI": [
"10.25103/jestr.132.19"
]
},
"num": null,
"urls": [],
"raw_text": "Santwana Sagnika, Anshuman Pattanaik, Bhabani Shankar Prasad Mishra, and Saroj K Meher. 2020. A review on multi-lingual sentiment analysis by ma- chine learning methods. Journal of Engineering Sci- ence & Technology Review, 13(2):154-166.",
"links": null
},
"BIBREF56": {
"ref_id": "b56",
"title": "Sentiment after translation: A case-study on Arabic social media posts",
"authors": [
{
"first": "Mohammad",
"middle": [],
"last": "Salameh",
"suffix": ""
},
{
"first": "Saif",
"middle": [],
"last": "Mohammad",
"suffix": ""
},
{
"first": "Svetlana",
"middle": [],
"last": "Kiritchenko",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "767--777",
"other_ids": {
"DOI": [
"10.3115/v1/N15-1078"
]
},
"num": null,
"urls": [],
"raw_text": "Mohammad Salameh, Saif Mohammad, and Svetlana Kiritchenko. 2015. Sentiment after translation: A case-study on Arabic social media posts. In Pro- ceedings of the 2015 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 767-777, Denver, Colorado. Association for Compu- tational Linguistics.",
"links": null
},
"BIBREF57": {
"ref_id": "b57",
"title": "Sanders-Twitter Sentiment Corpus",
"authors": [
{
"first": "J",
"middle": [],
"last": "Niek",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Sanders",
"suffix": ""
}
],
"year": 2011,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Niek J Sanders. 2011. Sanders-Twitter Sentiment Cor- pus. Sanders Analytics LLC.",
"links": null
},
"BIBREF58": {
"ref_id": "b58",
"title": "DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter",
"authors": [
{
"first": "Victor",
"middle": [],
"last": "Sanh",
"suffix": ""
},
{
"first": "Lysandre",
"middle": [],
"last": "Debut",
"suffix": ""
},
{
"first": "Julien",
"middle": [],
"last": "Chaumond",
"suffix": ""
},
{
"first": "Thomas",
"middle": [],
"last": "Wolf",
"suffix": ""
}
],
"year": 2020,
"venue": "Computing Research Repository",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1910.01108"
]
},
"num": null,
"urls": [],
"raw_text": "Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2020. DistilBERT, a distilled ver- sion of BERT: smaller, faster, cheaper and lighter. Computing Research Repository, arXiv:1910.01108. Version 4.",
"links": null
},
"BIBREF59": {
"ref_id": "b59",
"title": "Academic-industrial perspective on the development and deployment of a moderation system for a newspaper website",
"authors": [
{
"first": "Dietmar",
"middle": [],
"last": "Schabus",
"suffix": ""
},
{
"first": "Marcin",
"middle": [],
"last": "Skowron",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dietmar Schabus and Marcin Skowron. 2018. Academic-industrial perspective on the development and deployment of a moderation system for a newspaper website. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).",
"links": null
},
"BIBREF60": {
"ref_id": "b60",
"title": "Performing natural language processing on roman urdu datasets",
"authors": [
{
"first": "Zareen",
"middle": [],
"last": "Sharf",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Saif Ur Rahman",
"suffix": ""
}
],
"year": 2018,
"venue": "In International Journal of Computer Science and Network Security",
"volume": "18",
"issue": "",
"pages": "141--148",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zareen Sharf and Saif Ur Rahman. 2018. Performing natural language processing on roman urdu datasets. In International Journal of Computer Science and Network Security, volume 18, pages 141-148.",
"links": null
},
"BIBREF61": {
"ref_id": "b61",
"title": "Investigating societal biases in a poetry composition system",
"authors": [
{
"first": "Emily",
"middle": [],
"last": "Sheng",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Uthus",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the Second Workshop on Gender Bias in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "93--106",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Emily Sheng and David Uthus. 2020. Investigating societal biases in a poetry composition system. In Proceedings of the Second Workshop on Gender Bias in Natural Language Processing, pages 93-106, Barcelona, Spain (Online). Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF62": {
"ref_id": "b62",
"title": "Twoyear study of emotion and communication patterns in a highly polarized political discussion forum",
"authors": [
{
"first": "Pawel",
"middle": [],
"last": "Sobkowicz",
"suffix": ""
},
{
"first": "Antoni",
"middle": [],
"last": "Sobkowicz",
"suffix": ""
}
],
"year": 2012,
"venue": "Social Science Computer Review",
"volume": "30",
"issue": "4",
"pages": "448--469",
"other_ids": {
"DOI": [
"10.1177/0894439312436512"
]
},
"num": null,
"urls": [],
"raw_text": "Pawel Sobkowicz and Antoni Sobkowicz. 2012. Two- year study of emotion and communication patterns in a highly polarized political discussion forum. Social Science Computer Review, 30(4):448-469.",
"links": null
},
"BIBREF63": {
"ref_id": "b63",
"title": "What can we learn from almost a decade of food tweets",
"authors": [
{
"first": "Uga",
"middle": [],
"last": "Sprogis",
"suffix": ""
},
{
"first": "Matiss",
"middle": [],
"last": "Rikters",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:2007.05194"
]
},
"num": null,
"urls": [],
"raw_text": "Uga Sprogis and Matiss Rikters. 2020. What can we learn from almost a decade of food tweets. Comput- ing Research Repository, arXiv:2007.05194. Version 2.",
"links": null
},
"BIBREF64": {
"ref_id": "b64",
"title": "Multiemotions-it: a new dataset for opinion polarity and emotion analysis for italian",
"authors": [
{
"first": "Rachele",
"middle": [],
"last": "Sprugnoli",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the Seventh Italian Conference on Computational Linguistics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rachele Sprugnoli. 2020. Multiemotions-it: a new dataset for opinion polarity and emotion analysis for italian. In Proceedings of the Seventh Italian Conference on Computational Linguistics.",
"links": null
},
"BIBREF66": {
"ref_id": "b66",
"title": "Pythainlp/wisesight-sentiment: First release (v1.0). Zenodo",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.5281/zenodo.3457447"
]
},
"num": null,
"urls": [],
"raw_text": "Pythainlp/wisesight-sentiment: First release (v1.0). Zenodo.",
"links": null
},
"BIBREF67": {
"ref_id": "b67",
"title": "Dataset cartography: Mapping and diagnosing datasets with training dynamics",
"authors": [
{
"first": "Swabha",
"middle": [],
"last": "Swayamdipta",
"suffix": ""
},
{
"first": "Roy",
"middle": [],
"last": "Schwartz",
"suffix": ""
},
{
"first": "Nicholas",
"middle": [],
"last": "Lourie",
"suffix": ""
},
{
"first": "Yizhong",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Hannaneh",
"middle": [],
"last": "Hajishirzi",
"suffix": ""
},
{
"first": "Noah",
"middle": [
"A"
],
"last": "Smith",
"suffix": ""
},
{
"first": "Yejin",
"middle": [],
"last": "Choi",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "9275--9293",
"other_ids": {
"DOI": [
"10.18653/v1/2020.emnlp-main.746"
]
},
"num": null,
"urls": [],
"raw_text": "Swabha Swayamdipta, Roy Schwartz, Nicholas Lourie, Yizhong Wang, Hannaneh Hajishirzi, Noah A. Smith, and Yejin Choi. 2020. Dataset cartography: Mapping and diagnosing datasets with training dynamics. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9275-9293, Online. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF68": {
"ref_id": "b68",
"title": "Sentiment strength detection for the social web",
"authors": [
{
"first": "Mike",
"middle": [],
"last": "Thelwall",
"suffix": ""
},
{
"first": "Kevan",
"middle": [],
"last": "Buckley",
"suffix": ""
},
{
"first": "Georgios",
"middle": [],
"last": "Paltoglou",
"suffix": ""
}
],
"year": 2012,
"venue": "J. Am. Soc. Inf. Sci. Technol",
"volume": "63",
"issue": "1",
"pages": "163--173",
"other_ids": {
"DOI": [
"10.1002/asi.21662"
]
},
"num": null,
"urls": [],
"raw_text": "Mike Thelwall, Kevan Buckley, and Georgios Paltoglou. 2012. Sentiment strength detection for the social web. J. Am. Soc. Inf. Sci. Technol., 63(1):163-173.",
"links": null
},
"BIBREF70": {
"ref_id": "b70",
"title": "Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition",
"authors": [
{
"first": "Erik",
"middle": [
"F"
],
"last": "",
"suffix": ""
},
{
"first": "Tjong Kim",
"middle": [],
"last": "Sang",
"suffix": ""
}
],
"year": 2002,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Erik F. Tjong Kim Sang. 2002. Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition. In COLING-02: The 6th",
"links": null
},
"BIBREF72": {
"ref_id": "b72",
"title": "Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition",
"authors": [
{
"first": "Erik",
"middle": [
"F"
],
"last": "Tjong",
"suffix": ""
},
{
"first": "Kim",
"middle": [],
"last": "Sang",
"suffix": ""
},
{
"first": "Fien",
"middle": [],
"last": "De Meulder",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003",
"volume": "",
"issue": "",
"pages": "142--147",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Erik F. Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, pages 142- 147.",
"links": null
},
"BIBREF73": {
"ref_id": "b73",
"title": "A characterbased convolutional neural network for languageagnostic twitter sentiment analysis",
"authors": [
{
"first": "Joonatas",
"middle": [],
"last": "Wehrmann",
"suffix": ""
},
{
"first": "Willian",
"middle": [],
"last": "Becker",
"suffix": ""
},
{
"first": "E",
"middle": [
"L"
],
"last": "Henry",
"suffix": ""
},
{
"first": "Rodrigo",
"middle": [
"C"
],
"last": "Cagnini",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Barros",
"suffix": ""
}
],
"year": 2017,
"venue": "2017 International Joint Conference on Neural Networks (IJCNN)",
"volume": "",
"issue": "",
"pages": "2384--2391",
"other_ids": {
"DOI": [
"10.1109/IJCNN.2017.7966145"
]
},
"num": null,
"urls": [],
"raw_text": "Joonatas Wehrmann, Willian Becker, Henry E. L. Cagnini, and Rodrigo C. Barros. 2017. A character- based convolutional neural network for language- agnostic twitter sentiment analysis. In 2017 Interna- tional Joint Conference on Neural Networks (IJCNN), pages 2384-2391.",
"links": null
},
"BIBREF74": {
"ref_id": "b74",
"title": "Are all languages created equal in multilingual BERT?",
"authors": [
{
"first": "Shijie",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Mark",
"middle": [],
"last": "Dredze",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 5th Workshop on Representation Learning for NLP",
"volume": "",
"issue": "",
"pages": "120--130",
"other_ids": {
"DOI": [
"10.18653/v1/2020.repl4nlp-1.16"
]
},
"num": null,
"urls": [],
"raw_text": "Shijie Wu and Mark Dredze. 2020. Are all languages created equal in multilingual BERT? In Proceedings of the 5th Workshop on Representation Learning for NLP, pages 120-130, Online. Association for Com- putational Linguistics.",
"links": null
},
"BIBREF75": {
"ref_id": "b75",
"title": "Aditya Barua, and Colin Raffel. 2021. mT5: A massively multilingual pre-trained text-to-text transformer",
"authors": [
{
"first": "Linting",
"middle": [],
"last": "Xue",
"suffix": ""
},
{
"first": "Noah",
"middle": [],
"last": "Constant",
"suffix": ""
},
{
"first": "Adam",
"middle": [],
"last": "Roberts",
"suffix": ""
},
{
"first": "Mihir",
"middle": [],
"last": "Kale",
"suffix": ""
},
{
"first": "Rami",
"middle": [],
"last": "Al-Rfou",
"suffix": ""
},
{
"first": "Aditya",
"middle": [],
"last": "Siddhant",
"suffix": ""
}
],
"year": null,
"venue": "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "483--498",
"other_ids": {
"DOI": [
"10.18653/v1/2021.naacl-main.41"
]
},
"num": null,
"urls": [],
"raw_text": "Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, and Colin Raffel. 2021. mT5: A massively multilingual pre-trained text-to-text transformer. In Proceedings of the 2021 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, pages 483-498, On- line. Association for Computational Linguistics.",
"links": null
},
"BIBREF76": {
"ref_id": "b76",
"title": "Multilingual universal sentence encoder for semantic retrieval",
"authors": [
{
"first": "Yinfei",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Daniel",
"middle": [],
"last": "Cer",
"suffix": ""
},
{
"first": "Amin",
"middle": [],
"last": "Ahmad",
"suffix": ""
},
{
"first": "Mandy",
"middle": [],
"last": "Guo",
"suffix": ""
},
{
"first": "Jax",
"middle": [],
"last": "Law",
"suffix": ""
},
{
"first": "Noah",
"middle": [],
"last": "Constant",
"suffix": ""
},
{
"first": "Gustavo",
"middle": [],
"last": "Hernandez Abrego",
"suffix": ""
},
{
"first": "Steve",
"middle": [],
"last": "Yuan",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Tar",
"suffix": ""
},
{
"first": "Yun-Hsuan",
"middle": [],
"last": "Sung",
"suffix": ""
},
{
"first": "Brian",
"middle": [],
"last": "Strope",
"suffix": ""
},
{
"first": "Ray",
"middle": [],
"last": "Kurzweil",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations",
"volume": "",
"issue": "",
"pages": "87--94",
"other_ids": {
"DOI": [
"10.18653/v1/2020.acl-demos.12"
]
},
"num": null,
"urls": [],
"raw_text": "Yinfei Yang, Daniel Cer, Amin Ahmad, Mandy Guo, Jax Law, Noah Constant, Gustavo Hernandez Abrego, Steve Yuan, Chris Tar, Yun-hsuan Sung, Brian Strope, and Ray Kurzweil. 2020. Multilingual universal sen- tence encoder for semantic retrieval. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 87-94, Online. Association for Computational Linguistics.",
"links": null
},
"BIBREF77": {
"ref_id": "b77",
"title": "Improving multilingual sentence embedding using bidirectional dual encoder with additive margin softmax",
"authors": [
{
"first": "Yinfei",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Gustavo",
"middle": [],
"last": "Hernandez Abrego",
"suffix": ""
},
{
"first": "Steve",
"middle": [],
"last": "Yuan",
"suffix": ""
},
{
"first": "Mandy",
"middle": [],
"last": "Guo",
"suffix": ""
},
{
"first": "Qinlan",
"middle": [],
"last": "Shen",
"suffix": ""
},
{
"first": "Daniel",
"middle": [],
"last": "Cer",
"suffix": ""
},
{
"first": "Yun-Hsuan",
"middle": [],
"last": "Sung",
"suffix": ""
},
{
"first": "Brian",
"middle": [],
"last": "Strope",
"suffix": ""
},
{
"first": "Ray",
"middle": [],
"last": "Kurzweil",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19",
"volume": "",
"issue": "",
"pages": "5370--5378",
"other_ids": {
"DOI": [
"10.24963/ijcai.2019/746"
]
},
"num": null,
"urls": [],
"raw_text": "Yinfei Yang, Gustavo Hernandez Abrego, Steve Yuan, Mandy Guo, Qinlan Shen, Daniel Cer, Yun-hsuan Sung, Brian Strope, and Ray Kurzweil. 2019. Im- proving multilingual sentence embedding using bi- directional dual encoder with additive margin soft- max. In Proceedings of the Twenty-Eighth Inter- national Joint Conference on Artificial Intelligence, IJCAI-19, pages 5370-5378. International Joint Con- ferences on Artificial Intelligence Organization.",
"links": null
},
"BIBREF78": {
"ref_id": "b78",
"title": "The United Nations parallel corpus v1.0",
"authors": [
{
"first": "Micha\u0142",
"middle": [],
"last": "Ziemski",
"suffix": ""
},
{
"first": "Marcin",
"middle": [],
"last": "Junczys-Dowmunt",
"suffix": ""
},
{
"first": "Bruno",
"middle": [],
"last": "Pouliquen",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)",
"volume": "",
"issue": "",
"pages": "3530--3534",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Micha\u0142 Ziemski, Marcin Junczys-Dowmunt, and Bruno Pouliquen. 2016. The United Nations parallel cor- pus v1.0. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3530-3534, Portoro\u017e, Slovenia. European Language Resources Association (ELRA).",
"links": null
},
"BIBREF79": {
"ref_id": "b79",
"title": "Overview of the second BUCC shared task: Spotting parallel sentences in comparable corpora",
"authors": [
{
"first": "Pierre",
"middle": [],
"last": "Zweigenbaum",
"suffix": ""
},
{
"first": "Serge",
"middle": [],
"last": "Sharoff",
"suffix": ""
},
{
"first": "Reinhard",
"middle": [],
"last": "Rapp",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 10th Workshop on Building and Using Comparable Corpora",
"volume": "",
"issue": "",
"pages": "60--67",
"other_ids": {
"DOI": [
"10.18653/v1/W17-2512"
]
},
"num": null,
"urls": [],
"raw_text": "Pierre Zweigenbaum, Serge Sharoff, and Reinhard Rapp. 2017. Overview of the second BUCC shared task: Spotting parallel sentences in comparable corpora. In Proceedings of the 10th Workshop on Building and Using Comparable Corpora, pages 60-67, Vancou- ver, Canada. Association for Computational Linguis- tics.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"uris": null,
"type_str": "figure",
"text": "Nemenyi diagrams based on the ranking of models according to the F1-score on each dataset",
"num": null
},
"FIGREF1": {
"uris": null,
"type_str": "figure",
"text": "Detailed results of models' comparison.",
"num": null
},
"FIGREF2": {
"uris": null,
"type_str": "figure",
"text": "Results for models by their size and scenario.",
"num": null
},
"FIGREF3": {
"uris": null,
"type_str": "figure",
"text": "Results of multiple runs of fine-tuning experiments with different seeds.",
"num": null
},
"TABREF0": {
"html": null,
"num": null,
"content": "<table><tr><td>Model</td><td colspan=\"3\">Inf. time [s] #params #langs base a</td><td>data</td><td>reference</td></tr><tr><td>mT5</td><td>1.69</td><td>277M</td><td>101 T5</td><td>CC b</td><td>(Xue et al., 2021)</td></tr><tr><td>LASER</td><td>1.64</td><td>52M</td><td>93 BiLSTM</td><td>OPUS c</td><td>(Artetxe and Schwenk, 2019)</td></tr><tr><td>mBERT</td><td>1.49</td><td>177M</td><td>104 BERT</td><td>Wiki</td><td>(Devlin et al., 2019)</td></tr><tr><td>MPNet**</td><td>1.38</td><td>278M</td><td>53 XLM-R</td><td colspan=\"2\">OPUS c , MUSE d , Wikititles e (Reimers and Gurevych, 2020)</td></tr><tr><td>XLM-R-dist**</td><td>1.37</td><td>278M</td><td>53 XLM-R</td><td colspan=\"2\">OPUS c , MUSE d , Wikititles e (Reimers and Gurevych, 2020)</td></tr><tr><td>XLM-R</td><td>1.37</td><td>278M</td><td>100 XLM-R</td><td>CC</td><td>(Conneau et al., 2020)</td></tr><tr><td>LaBSE</td><td>1.36</td><td>470M</td><td>109 BERT</td><td>CC, Wiki + mined bitexts</td><td>(Feng et al., 2020)</td></tr><tr><td>DistilmBERT</td><td>0.79</td><td>134M</td><td>104 BERT</td><td>Wiki</td><td>(Sanh et al., 2020)</td></tr><tr><td>mUSE-dist**</td><td>0.79</td><td>134M</td><td colspan=\"3\">53 DistilmBERT OPUS c , MUSE d , Wikititles e (Reimers and Gurevych, 2020)</td></tr><tr><td>mUSE-transformer*</td><td>0.65</td><td>85M</td><td>16 transformer</td><td>mined QA + bitexts, SNLI</td><td>(Yang et al., 2020)</td></tr><tr><td>mUSE-cnn*</td><td>0.12</td><td>68M</td><td>16 CNN</td><td>mined QA + bitexts, SNLI</td><td>(Yang et al., 2020)</td></tr><tr><td colspan=\"6\">*mUSE models were used in TensorFlow implementation in contrast to others in torch a Base model is either monolingual version on which it was based or another multilingual model which was used and adopted b Colossal Clean Crawled Corpus in multilingual version (mC4) c multiple datasets from OPUS website (https://opus.nlpl.eu), d bilingual dictionaries from MUSE (https://github.com/facebookresearch/MUSE), e just titles from wiki articles in multiple languages</td></tr></table>",
"text": "Models used in experiments -inference times, number of parameters, and languages used in pre-training, base model and data used in pre-training",
"type_str": "table"
},
"TABREF1": {
"html": null,
"num": null,
"content": "<table><tr><td/><td>Count</td><td>Category</td><td/><td/><td>Samples</td><td/><td/><td>Mean #</td></tr><tr><td/><td/><td colspan=\"2\">N O R SM</td><td>NEG</td><td>NEU</td><td colspan=\"3\">POS words characters</td></tr><tr><td>English</td><td>17</td><td>3 4 4</td><td>6</td><td colspan=\"3\">305,782 289,847 1,734,857</td><td>42</td><td>233</td></tr><tr><td>Arabic</td><td>9</td><td>0 1 4</td><td>4</td><td colspan=\"2\">139,173 192,463</td><td>600,439</td><td>28</td><td>159</td></tr><tr><td>Spanish</td><td>5</td><td>0 1 3</td><td>2</td><td colspan=\"2\">110,156 120,668</td><td>188,068</td><td>145</td><td>864</td></tr><tr><td>Chinese</td><td>2</td><td>0 0 2</td><td>0</td><td>118,023</td><td>68,953</td><td>144,726</td><td>48</td><td>-</td></tr><tr><td>German</td><td>6</td><td>0 0 1</td><td>5</td><td>105,416</td><td>99,291</td><td>111,180</td><td>19</td><td>131</td></tr><tr><td>Polish</td><td>4</td><td>0 0 2</td><td>2</td><td>78,309</td><td>61,041</td><td>97,338</td><td>39</td><td>245</td></tr><tr><td>French</td><td>3</td><td>0 0 1</td><td>2</td><td>84,324</td><td>43,097</td><td>83,210</td><td>19</td><td>108</td></tr><tr><td>Japanese</td><td>1</td><td>0 0 1</td><td>0</td><td>83,985</td><td>41,976</td><td>83,819</td><td>60</td><td>-</td></tr><tr><td>Czech</td><td>4</td><td>0 0 2</td><td>2</td><td>39,687</td><td>59,181</td><td>97,419</td><td>29</td><td>168</td></tr><tr><td>Portuguese</td><td>4</td><td>0 0 0</td><td>4</td><td>57,737</td><td>54,145</td><td>45,952</td><td>12</td><td>73</td></tr><tr><td>Slovenian</td><td>2</td><td>1 0 0</td><td>1</td><td>34,178</td><td>50,055</td><td>29,310</td><td>161</td><td>1054</td></tr><tr><td>Russian</td><td>2</td><td>0 0 0</td><td>2</td><td>32,018</td><td>47,852</td><td>31,060</td><td>11</td><td>73</td></tr><tr><td>Croatian</td><td>2</td><td>1 0 0</td><td>1</td><td>19,907</td><td>19,298</td><td>38,389</td><td>86</td><td>556</td></tr><tr><td>Serbian</td><td>3</td><td>0 0 2</td><td>1</td><td>25,580</td><td>31,762</td><td>19,026</td><td>176</td><td>1094</td></tr><tr><td>Thai</td><td>2</td><td>0 0 1</td><td>1</td><td>9,327</td><td>28,615</td><td>34,377</td><td>18</td><td>317</td></tr><tr><td>Bulgarian</td><td>1</td><td>0 0 0</td><td>1</td><td>14,040</td><td>28,543</td><td>19,567</td><td>12</td><td>85</td></tr><tr><td>Hungarian</td><td>1</td><td>0 0 0</td><td>1</td><td>9,004</td><td>17,590</td><td>30,088</td><td>11</td><td>83</td></tr><tr><td>Slovak</td><td>1</td><td>0 0 0</td><td>1</td><td>14,518</td><td>12,735</td><td>29,370</td><td>13</td><td>97</td></tr><tr><td>Albanian</td><td>1</td><td>0 0 0</td><td>1</td><td>6,958</td><td>14,675</td><td>22,651</td><td>13</td><td>90</td></tr><tr><td>Swedish</td><td>1</td><td>0 0 0</td><td>1</td><td>16,664</td><td>12,912</td><td>11,770</td><td>14</td><td>94</td></tr><tr><td>Bosnian</td><td>1</td><td>0 0 0</td><td>1</td><td>12,078</td><td>11,039</td><td>13,066</td><td>12</td><td>75</td></tr><tr><td>Urdu</td><td>1</td><td>0 1 0</td><td>0</td><td>5,244</td><td>8,580</td><td>5,836</td><td>13</td><td>69</td></tr><tr><td>Hindi</td><td>1</td><td>0 0 0</td><td>1</td><td>4,992</td><td>6,392</td><td>5,615</td><td>27</td><td>128</td></tr><tr><td>Persian</td><td>1</td><td>0 0 1</td><td>0</td><td>1,619</td><td>5,074</td><td>6,832</td><td>21</td><td>104</td></tr><tr><td>Italian</td><td>2</td><td>0 0 0</td><td>2</td><td>4,043</td><td>4,193</td><td>3,829</td><td>16</td><td>104</td></tr><tr><td>Hebrew</td><td>1</td><td>0 0 0</td><td>1</td><td>2,283</td><td>238</td><td>6,098</td><td>22</td><td>110</td></tr><tr><td>Latvian</td><td>1</td><td>0 0 0</td><td>1</td><td>1,379</td><td>2,617</td><td>1,794</td><td>20</td><td>138</td></tr></table>",
"text": "Summary of 80 high-quality datasets selected. Categories: N -News, O -Other, R -Reviews, SM -Social Media",
"type_str": "table"
},
"TABREF2": {
"html": null,
"num": null,
"content": "<table><tr><td>pl</td><td colspan=\"2\">2968 14% 60% 26%</td></tr><tr><td>en</td><td>943</td><td>4% 74% 22%</td></tr></table>",
"text": "Statistics of the internal dataset lang samples NEG NEU POS",
"type_str": "table"
},
"TABREF3": {
"html": null,
"num": null,
"content": "<table><tr><td>XLM-R</td><td>LaBSE</td><td>MPNet</td><td>XLM-R-dist</td><td>mT5</td><td>mBERT</td><td>DistilmBERT</td><td>mUSE-dist</td><td>LASER</td><td>mUSE-trans.</td><td>mUSE-cnn</td></tr><tr><td/><td/><td/><td colspan=\"4\">Just Head -Linear</td><td/><td/><td/><td/></tr><tr><td colspan=\"11\">W 62 62 63 60 59 56 55 59 55 55 54 A 51 54 55 51 49 45 43 50 47 47 45 I 55 61 61 56 50 43 38 60 50 49 50</td></tr><tr><td/><td/><td/><td colspan=\"5\">Just Head -BiLSTM</td><td/><td/><td/></tr><tr><td colspan=\"8\">W 66 62 63 62 65 60 59 62 A 57 55 56 54 56 49 48 54 I 64 63 64 63 63 54 48 64</td><td>---</td><td>---</td><td>---</td></tr><tr><td/><td/><td/><td/><td colspan=\"3\">Fine-tuning</td><td/><td/><td/><td/></tr><tr><td colspan=\"8\">W 68 68 67 67 66 65 64 63 A 61 62 62 62 60 56 56 56 I 70 69 65 67 67 57 58 60</td><td>---</td><td>---</td><td>---</td></tr></table>",
"text": "Aggregated results of models (F1 score in %). The best results for each test set are highlighted. (Wwhole test, A -avg. by dataset, I -internal)",
"type_str": "table"
},
"TABREF4": {
"html": null,
"num": null,
"content": "<table><tr><td>(Al Omari et al., 2019)</td><td>ar</td><td>R</td><td>No</td><td>3096</td><td>13.0/10.2/76.8</td><td>9</td><td>51</td></tr><tr><td>(Elnagar et al., 2018)</td><td>ar</td><td>R</td><td>No</td><td>400101</td><td>13.0/19.9/67.1</td><td>22</td><td>127</td></tr><tr><td>(Aly and Atiya, 2013)</td><td>ar</td><td>R</td><td>No</td><td>6250</td><td>11.6/17.9/70.5</td><td>65</td><td>343</td></tr><tr><td>(Elnagar and Einea, 2016)</td><td>ar</td><td>R</td><td>No</td><td>504007</td><td>15.4/21.0/63.6</td><td>77</td><td>424</td></tr><tr><td>(Baly et al., 2018)</td><td>ar</td><td colspan=\"2\">SM Yes</td><td>2809</td><td>47.2/23.9/29.0</td><td>22</td><td>130</td></tr></table>",
"text": "List of all monolingual datasets used in experiments. Category (Cat.): R -Reviews, SM -Social Media, C -Chats, N -News, P -Poems, M -Mixed. HL -human labeled, #Words and #Chars are mean valuesPaperLang Cat. HL Samples NEG/NEU/POS #Words #Char.",
"type_str": "table"
},
"TABREF6": {
"html": null,
"num": null,
"content": "<table><tr><td>Paper</td><td colspan=\"7\">Cat. Lang HL Samples (NEG/NEU/POS) #Words #Char.</td></tr><tr><td>(Narr et al., 2012)</td><td>SM</td><td>de</td><td>Yes</td><td>953</td><td>10.0/75.1/14.9</td><td>12</td><td>80</td></tr><tr><td/><td/><td>de</td><td>Yes</td><td>1781</td><td>16.9/63.3/19.8</td><td>13</td><td>81</td></tr><tr><td/><td/><td>en</td><td>Yes</td><td>7073</td><td>17.4/60.0/22.6</td><td>14</td><td>78</td></tr><tr><td/><td/><td>fr</td><td>Yes</td><td>685</td><td>23.4/53.4/23.2</td><td>14</td><td>82</td></tr><tr><td/><td/><td>fr</td><td>Yes</td><td>1786</td><td>25.0/54.3/20.8</td><td>15</td><td>83</td></tr><tr><td/><td/><td>pt</td><td>Yes</td><td>759</td><td>28.1/33.2/38.7</td><td>14</td><td>78</td></tr><tr><td/><td/><td>pt</td><td>Yes</td><td>1769</td><td>30.7/33.9/35.4</td><td>14</td><td>78</td></tr><tr><td>(Keung et al., 2020)</td><td>R</td><td>de</td><td>No</td><td>209073</td><td>40.1/20.0/39.9</td><td>33</td><td>208</td></tr><tr><td/><td/><td>en</td><td>No</td><td>209393</td><td>40.0/20.0/40.0</td><td>34</td><td>179</td></tr><tr><td/><td/><td>es</td><td>No</td><td>208127</td><td>40.2/20.0/39.8</td><td>27</td><td>152</td></tr><tr><td/><td/><td>fr</td><td>No</td><td>208160</td><td>40.2/20.1/39.7</td><td>28</td><td>160</td></tr><tr><td/><td/><td>ja</td><td>No</td><td>209780</td><td>40.0/20.0/40.0</td><td>2</td><td>101</td></tr><tr><td/><td/><td>zh</td><td>No</td><td>205977</td><td>39.8/20.1/40.1</td><td>1</td><td>50</td></tr><tr><td colspan=\"2\">(Rosenthal et al., 2017) M</td><td>ar</td><td>Yes</td><td>9391</td><td>35.5/40.6/23.9</td><td>14</td><td>105</td></tr><tr><td/><td/><td>en</td><td>Yes</td><td>65071</td><td>19.1/45.7/35.2</td><td>18</td><td>111</td></tr><tr><td>(Patwa et al., 2020)</td><td>SM</td><td>es</td><td>Yes</td><td>14920</td><td>16.8/33.1/50.0</td><td>16</td><td>86</td></tr><tr><td/><td/><td>hi</td><td>Yes</td><td>16999</td><td>29.4/37.6/33.0</td><td>27</td><td>128</td></tr><tr><td>(Mozeti\u010d et al., 2016)</td><td>SM</td><td>bg</td><td>Yes</td><td>62150</td><td>22.6/45.9/31.5</td><td>12</td><td>85</td></tr><tr><td/><td/><td>bs</td><td>Yes</td><td>36183</td><td>33.4/30.5/36.1</td><td>12</td><td>75</td></tr><tr><td/><td/><td>de</td><td>Yes</td><td>90534</td><td>19.7/52.8/27.4</td><td>12</td><td>94</td></tr><tr><td/><td/><td>en</td><td>Yes</td><td>85784</td><td>26.8/44.1/29.1</td><td>12</td><td>77</td></tr><tr><td/><td/><td>es</td><td colspan=\"2\">Yes 191412</td><td>11.8/37.9/50.3</td><td>14</td><td>92</td></tr><tr><td/><td/><td>hr</td><td>Yes</td><td>75569</td><td>25.7/23.9/50.4</td><td>12</td><td>91</td></tr><tr><td/><td/><td>hu</td><td>Yes</td><td>56682</td><td>15.9/31.0/53.1</td><td>11</td><td>83</td></tr><tr><td/><td/><td>pl</td><td colspan=\"2\">Yes 168931</td><td>30.0/26.1/43.9</td><td>11</td><td>82</td></tr><tr><td/><td/><td>pt</td><td colspan=\"2\">Yes 145197</td><td>37.2/35.0/27.8</td><td>10</td><td>61</td></tr><tr><td/><td/><td>ru</td><td>Yes</td><td>87704</td><td>32.0/40.1/27.8</td><td>10</td><td>67</td></tr><tr><td/><td/><td>sk</td><td>Yes</td><td>56623</td><td>25.6/22.5/51.9</td><td>13</td><td>97</td></tr><tr><td/><td/><td>sl</td><td colspan=\"2\">Yes 103126</td><td>29.9/43.3/26.8</td><td>13</td><td>91</td></tr><tr><td/><td/><td>sq</td><td>Yes</td><td>44284</td><td>15.7/33.1/51.1</td><td>13</td><td>90</td></tr><tr><td/><td/><td>sr</td><td>Yes</td><td>67696</td><td>34.8/42.8/22.4</td><td>13</td><td>81</td></tr><tr><td/><td/><td>sv</td><td>Yes</td><td>41346</td><td>40.3/31.2/28.5</td><td>14</td><td>94</td></tr></table>",
"text": "List of all multilingual datasets used in experiments. Category (Cat.): R -Reviews, SM -Social Media, C -Chats, N -News, P -Poems, M -Mixed. HL -human labeled",
"type_str": "table"
}
}
}
} |