File size: 106,779 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 |
{
"paper_id": "2021",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T06:07:28.889423Z"
},
"title": "Lightweight Models for Multimodal Sequential Data",
"authors": [
{
"first": "Soumya",
"middle": [],
"last": "Sourav",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "The University of Texas at Dallas",
"location": {}
},
"email": ""
},
{
"first": "Jessica",
"middle": [],
"last": "Ouyang",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "The University of Texas at Dallas",
"location": {}
},
"email": "[email protected]"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Human language encompasses more than just text; it also conveys emotions through tone and gestures. We present a case study of three simple and efficient Transformer-based architectures for predicting sentiment and emotion in multimodal data. The Late Fusion model merges unimodal features to create a multimodal feature sequence, the Round Robin model iteratively combines bimodal features using cross-modal attention, and the Hybrid Fusion model combines trimodal and unimodal features together to form a final feature sequence for predicting sentiment. Our experiments show that our small models are effective and outperform the publicly released versions of much larger, state-of-the-art multimodal sentiment analysis systems.",
"pdf_parse": {
"paper_id": "2021",
"_pdf_hash": "",
"abstract": [
{
"text": "Human language encompasses more than just text; it also conveys emotions through tone and gestures. We present a case study of three simple and efficient Transformer-based architectures for predicting sentiment and emotion in multimodal data. The Late Fusion model merges unimodal features to create a multimodal feature sequence, the Round Robin model iteratively combines bimodal features using cross-modal attention, and the Hybrid Fusion model combines trimodal and unimodal features together to form a final feature sequence for predicting sentiment. Our experiments show that our small models are effective and outperform the publicly released versions of much larger, state-of-the-art multimodal sentiment analysis systems.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Language is composed of three different modalities: text, audio, and video. These three modalities together make it easier for humans to convey emotion and sentiment. Thus, a machine learning model for sentiment analysis needs to learn the features and interactions of all three modalities. For example, a frown in the video can alter the emotion expressed in the text transcript, or audio intensity can help determine if a speaker is getting agitated.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Multimodal learning has recently received a good deal of attention from the natural language processing community [Sun et al., 2016 , Chen et al., 2018 , Pham et al., 2019 . The Transformer network [Vaswani et al., 2017] , with its self-attention modules, has achieved strong performance in multimodal learning; attention provides a natural way to model the relationship between pairs of modalities.",
"cite_spans": [
{
"start": 114,
"end": 131,
"text": "[Sun et al., 2016",
"ref_id": "BIBREF13"
},
{
"start": 132,
"end": 151,
"text": ", Chen et al., 2018",
"ref_id": "BIBREF3"
},
{
"start": 152,
"end": 171,
"text": ", Pham et al., 2019",
"ref_id": "BIBREF12"
},
{
"start": 198,
"end": 220,
"text": "[Vaswani et al., 2017]",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this work we investigate three small, lightweight, Transformer-based architectures for multimodal sentiment analysis and emotion recog-nition. Our first model is an implementation of the Late Fusion model commonly used as a baseline system, which assigns individual Transformer blocks to each of the three modalities for feature extraction and then combines these unimodal features to learn cross-modal interactions. The second model is an implementation of the Round Robin approach; the model generates bimodal features by using cross-modal attention to combine pairs of modalities, one pair at a time. Our last model is a Hybrid of the early and late fusion schemes. This model merges the features extracted using a late fusion pipeline, as well as those from an early fusion pipeline, where the three modalities are concatenated and passed through a single Transformer block for feature extraction;.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We present experiments using these three models on three multimodal datasets: IEMOCAP [Busso et al., 2008] , an emotion recognition dataset, and CMU-MOSI [Zadeh et al., 2016] and CMU-MOSEI [Zadeh et al., 2018b] , two multimodal sentiment analysis datasets. Our results show that our small models are competitive with state-of-the-art models that use much more complex architectures.",
"cite_spans": [
{
"start": 86,
"end": 106,
"text": "[Busso et al., 2008]",
"ref_id": "BIBREF2"
},
{
"start": 154,
"end": 174,
"text": "[Zadeh et al., 2016]",
"ref_id": "BIBREF21"
},
{
"start": 189,
"end": 210,
"text": "[Zadeh et al., 2018b]",
"ref_id": "BIBREF23"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our main contributions are as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 We present three lightweight architectures for multimodal sentiment analysis that achieve comparable results to much larger, state-ofthe-art models.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 We analyze the effect of removing or simplifying components of state-of-the-art multimodal architectures.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 We conduct experiments on small training sets, demonstrating the ability of our lightweight architectures to leverage limited training data and computational resources.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We do not give an exhaustive list of prior work in multimodal sentiment analysis, but focus on recent neural approaches that achieved state-of-the-art performance at their times of publication.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "The Memory Fusion Network (MFN) of Zadeh et al. [2018a] uses a separate LSTM to encode each of the three modalities and then uses attention to model cross-modal interactions for different combinations of modalities. The Recurrent Attended Variation Embedding Network (RAVEN) of Wang et al. [2019] encodes the audio and video features using two recurrent neural networks; these features are combined with the textual input using cross-modal attention in a Gated Modality Mixing Network. The Multi-Attention Recurrent Network (MARN) of Zadeh et al. [2018c] is an LSTM-based architecture that stores representations of each of the three modalities, which are then combined using a multi-attention block. Finally, the Multimodal Cyclic Translation Network (MCTN) of Pham et al.",
"cite_spans": [
{
"start": 35,
"end": 55,
"text": "Zadeh et al. [2018a]",
"ref_id": "BIBREF22"
},
{
"start": 278,
"end": 296,
"text": "Wang et al. [2019]",
"ref_id": "BIBREF18"
},
{
"start": 534,
"end": 554,
"text": "Zadeh et al. [2018c]",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Recurrent Network Approaches",
"sec_num": "2.1"
},
{
"text": "[2019] produces multimodal features by translating one modality into another, learning a joint encoding in that direction, and then back-translating to learn a joint encoding in the other direction.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Recurrent Network Approaches",
"sec_num": "2.1"
},
{
"text": "The Transformer network [Vaswani et al., 2017] has been used widely in neural machine translation [Tubay and Costa-juss\u00e0, 2018 , Edunov et al., 2018 , Xia et al., 2019 , Devlin et al., 2019 and has proven effective for sentiment analysis and emotion recognition. However, existing architectures are very dense compared to our three lightweight models. The Multimodal Transformer (MuLT) of Tsai et al. [2019] modifies the Transformer block to compute cross-modal attention for two modalities at a time. It combines modalities in directed pairs, using a total of six Transformers, whose outputs are then merged into a single multimodal representation. Unlike other works, MuLT is able to handle cases where the three modalities are not aligned at the word level; it learns soft alignments via the cross-modal attention weights for each pair of modalities. The model works well in the unaligned case, and in the aligned case, it gives state of the art performance the Happy emotion in IEMO-CAP.",
"cite_spans": [
{
"start": 24,
"end": 46,
"text": "[Vaswani et al., 2017]",
"ref_id": "BIBREF17"
},
{
"start": 98,
"end": 126,
"text": "[Tubay and Costa-juss\u00e0, 2018",
"ref_id": "BIBREF16"
},
{
"start": 127,
"end": 148,
"text": ", Edunov et al., 2018",
"ref_id": "BIBREF6"
},
{
"start": 149,
"end": 167,
"text": ", Xia et al., 2019",
"ref_id": "BIBREF19"
},
{
"start": 168,
"end": 189,
"text": ", Devlin et al., 2019",
"ref_id": "BIBREF5"
},
{
"start": 389,
"end": 407,
"text": "Tsai et al. [2019]",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Transformer Network Approaches",
"sec_num": "2.2"
},
{
"text": "The Factorized Multimodal Transformer (FMT)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Transformer Network Approaches",
"sec_num": "2.2"
},
{
"text": "of introduces Factorized Multimodal Self-Attention (FSM) modules, which compute self-attention over unimodal, bimodal, and trimodal inputs in parallel. FMT gives state of the art performance in the word-aligned case on CMU-MOSI and on the Sad, Angry, and Neutral emotions in IEMOCAP. We use FMT, along with the word-aligned version of MuLT, as baselines for comparison in our experiments.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Transformer Network Approaches",
"sec_num": "2.2"
},
{
"text": "The Interaction Canonical Correlation Network (ICCN) [Sun et al., 2020] implements Deep Canonical Correlation Analysis (DCCA) [Andrew et al., 2013] to extract bimodal features from the outer product matrix of a pair of modalities. Sun et al. use two pairs, text with audio and text with video;",
"cite_spans": [
{
"start": 53,
"end": 71,
"text": "[Sun et al., 2020]",
"ref_id": "BIBREF14"
},
{
"start": 126,
"end": 147,
"text": "[Andrew et al., 2013]",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Canonical Correlation Approach",
"sec_num": "2.3"
},
{
"text": "these \"text-based audio\" and \"text-based video\" features are concatenated with purely textual features to form a multimodal embedding for sentiment analysis. ICCN gives state-of-the-art performance on CMU-MOSEI and on the Sad emotion in IEMOCAP.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Canonical Correlation Approach",
"sec_num": "2.3"
},
{
"text": "3 Models",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Canonical Correlation Approach",
"sec_num": "2.3"
},
{
"text": "We use T , A, and V , to represent the three modalities: text, audio, and video, respectively. Following the notation in [Tsai et al., 2019] and , we denote the input as",
"cite_spans": [
{
"start": 121,
"end": 140,
"text": "[Tsai et al., 2019]",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Input Alignment",
"sec_num": "3.1"
},
{
"text": "X T,A,V = {x T , x A , x V }",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input Alignment",
"sec_num": "3.1"
},
{
"text": "where",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input Alignment",
"sec_num": "3.1"
},
{
"text": "x i = [x t,i ] for i \u2208 [T, A, V ] and t \u2208 [1, \u03c4 ]",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input Alignment",
"sec_num": "3.1"
},
{
"text": "and \u03c4 is the length of the input sentence. Each of the three modalities has its own lowlevel features, such as the Mel spectrogram for audio or facial landmarks for video. These features are extracted at different sampling rates -one set of features per word or character for text, per millisecond for audio, and per frame for video -and thus the input sequences for the three modalities are often different. A five-thousand-millisecond audio sequence, for example, may be only a three-word sequence from a textual perspective and a 50-frame sequence from a video perspective.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input Alignment",
"sec_num": "3.1"
},
{
"text": "We align the audio and video to the text using the timestamps provided in the text transcripts. The set of audio or video samples that correspond to a word in the transcript are combined using a series of 1D convolutional layers:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input Alignment",
"sec_num": "3.1"
},
{
"text": "X {T,A,V } = conv1D X {T,A,V } \u2208 R d",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input Alignment",
"sec_num": "3.1"
},
{
"text": "where d is a common feature dimension size. This procedure ensures that the input sequence length is the same across modalities.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input Alignment",
"sec_num": "3.1"
},
{
"text": "Our three lightweight architectures are comprised of Transformer blocks [Vaswani et al., 2017] , which are non-recurrent neural networks that can process sequential data. It consists of alternating attention and linear layers. The attention block of a Transformer uses multi-head attention, where each head computes scaled dot product attention:",
"cite_spans": [
{
"start": 72,
"end": 94,
"text": "[Vaswani et al., 2017]",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Transformer Blocks",
"sec_num": "3.2"
},
{
"text": "attn(Q, K, V ) = softmax QK T \u221a d k V head i = attn QW Q i , KW K i , V W V i multi(Q, K, V ) = [head 1 ; . . . ; head h ]W O",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Transformer Blocks",
"sec_num": "3.2"
},
{
"text": "where Q, K, V represent the query, key and value; d k is the key dimension size;",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Transformer Blocks",
"sec_num": "3.2"
},
{
"text": "W Q i , W K i , W V",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Transformer Blocks",
"sec_num": "3.2"
},
{
"text": "i are learned projection matrices for head i; and W O is a learned projection matrix for the attention block.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Transformer Blocks",
"sec_num": "3.2"
},
{
"text": "In addition, Vaswani et al. note that positional encodings must be added to Transformer input because there is no sequential information present in the Transformer itself: Figure 1 shows our Late Fusion architecture. Three unimodal Transformers learn high-level features from the low-level input features of each modality. The outputs of these unimodal Transformers are then merged together using a simple summation, rather than the merge layer used in previous work [Tsai et al., 2019] , and passed to a residual network of linear layers [Xie et al., 2017] for sentiment prediction. Figure 2 shows our Round Robin architecture, which is a simplification of MuTL [Tsai et al., 2019] . Three cross-modal Transformers learn bimodal feaatures for ordered pairs of modalities, where the query is one modality and the key/value is the other. We use only three pairs -text query and audio key/value, audio query and video key/value, and video query and text key/valuewith bimodal information flowing in only one direction; in contrast, MuLT uses six pairs of crossmodal Transformers, with information flowing in both directions. MuLT also uses three Transformers, one for each modality, to merge the two pairs sharing that modality as key/value; our pairwise features are simply concatenated and passed to the output residual network. Figure 3 shows our Hybrid Fusion architecture, which uses both an early fusion approach that concatenates the inputs and passes them to a single Transformer to learn trimodal features, as well as a late fusion approach that passes each modality through a separate Transformer to learn unimodal features. The trimodal and unimodal features are concatenated together and merged using a layer of Gated Recurrent Units . ",
"cite_spans": [
{
"start": 467,
"end": 486,
"text": "[Tsai et al., 2019]",
"ref_id": "BIBREF15"
},
{
"start": 539,
"end": 557,
"text": "[Xie et al., 2017]",
"ref_id": "BIBREF20"
},
{
"start": 663,
"end": 682,
"text": "[Tsai et al., 2019]",
"ref_id": "BIBREF15"
}
],
"ref_spans": [
{
"start": 172,
"end": 180,
"text": "Figure 1",
"ref_id": "FIGREF0"
},
{
"start": 584,
"end": 592,
"text": "Figure 2",
"ref_id": "FIGREF1"
},
{
"start": 1329,
"end": 1337,
"text": "Figure 3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Transformer Blocks",
"sec_num": "3.2"
},
{
"text": "P E (pos,2i) = sin(pos/10000 2i/d model ) P E (pos,2i+1) = cos(pos/10000 2i/d model ) X =X + P E",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Transformer Blocks",
"sec_num": "3.2"
},
{
"text": "We train our models on a single NVIDIA K80 GPU. We tune hyperparameter values for our model using the validation sets provided by our evaluation datasets; we achieve the best validation performance using 8 attention blocks per Transformer, each with 5 attention heads, and a hidden size was set to 40. The dropout rate was set to 0.15; the best learning rate for IEMOCAP was 0.02, while for CMU-MOSI and CMU-MOSEI it was 0.01, with batch sizes of 32, 128, and 40, respectively.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "4"
},
{
"text": "IEMOCAP [Busso et al., 2008] consists of video recordings of 151 conversation sessions (dialogues), totaling around 6k verbal interactions. This dataset is intended for multilabel emotion classification; we evaluate on the four labeled emotions (Happy, Sad, Angry, and Neutral) used in previous work [Wang et al., 2019] ; also following previous work, we report binary accuracy and F1 score as the evaluation metrics on this dataset.",
"cite_spans": [
{
"start": 8,
"end": 28,
"text": "[Busso et al., 2008]",
"ref_id": "BIBREF2"
},
{
"start": 300,
"end": 319,
"text": "[Wang et al., 2019]",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "CMU-MOSI [Zadeh et al., 2016 ] is a sentiment analysis dataset of 2199 short monologues labeled in the range [\u22123, 3], with \u22123 being strongly negative and +3 being strongly positive. Following previous work, we report seven-class and binary accuracy, F1 score, mean absolute error, and correlation with human judgments.",
"cite_spans": [
{
"start": 9,
"end": 28,
"text": "[Zadeh et al., 2016",
"ref_id": "BIBREF21"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "CMU-MOSEI [Zadeh et al., 2018b ] is a sentiment and emotion analysis dataset of 23K movie reviews from YouTube. As with CMU-MOSI, it is labeled in the range of [\u22123, 3], and its evaluation metrics are the same as in CMU-MOSI.",
"cite_spans": [
{
"start": 10,
"end": 30,
"text": "[Zadeh et al., 2018b",
"ref_id": "BIBREF23"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "Text Features: For word-level textual features we use the pretrained, 300-dimensional, Common Crawl GloVe embeddings [Pennington et al., 2014] .",
"cite_spans": [
{
"start": 117,
"end": 142,
"text": "[Pennington et al., 2014]",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Features and Alignment",
"sec_num": "4.2"
},
{
"text": "Audio features, including Mel-frequency cepstral coefficients and transformations thereof, as well as harmonic, percussive, and glottal source parameters. We also use COVERAP [Degottex et al., 2014 ] to extract pitch tracking and voiced/unvoiced sloping parameters, peak slope parameters, and maximum dispersion quotients. Video Features: We extract 35 facial units using Facet [iMotions, 2017] , as well as 35 facial action units and 30 facial landmark and gaze fea-tures using OpenFace [Baltrusaitis et al., 2018] .",
"cite_spans": [
{
"start": 175,
"end": 197,
"text": "[Degottex et al., 2014",
"ref_id": "BIBREF4"
},
{
"start": 378,
"end": 394,
"text": "[iMotions, 2017]",
"ref_id": null
},
{
"start": 488,
"end": 515,
"text": "[Baltrusaitis et al., 2018]",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Features and Alignment",
"sec_num": "4.2"
},
{
"text": "We compare our results with the state-of-the-art Multimodal Transformer (MuLT) 1 [Tsai et al., 2019] and Factorized Multimodal Transformer (FMT) , as well as Memory Fusion Network (MFN) [Zadeh et al., 2018a] , Recurrent Attended Variation Embedding Network (RAVEN) [Wang et al., 2019] , Multi-Attention Recurrent Network (MARN) [Zadeh et al., 2018c] , and Multimodal Cyclic Translation Network (MCTN) [Pham et al., 2019] . These systems are described in Section 2; all attained state of the art on at least one of the evaluation datasets at their times of publication, and all use a similar feature set to our work.",
"cite_spans": [
{
"start": 81,
"end": 100,
"text": "[Tsai et al., 2019]",
"ref_id": "BIBREF15"
},
{
"start": 186,
"end": 207,
"text": "[Zadeh et al., 2018a]",
"ref_id": "BIBREF22"
},
{
"start": 265,
"end": 284,
"text": "[Wang et al., 2019]",
"ref_id": "BIBREF18"
},
{
"start": 328,
"end": 349,
"text": "[Zadeh et al., 2018c]",
"ref_id": null
},
{
"start": 401,
"end": 420,
"text": "[Pham et al., 2019]",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Baseline Models",
"sec_num": "4.3"
},
{
"text": "We present the results of our model compared to the reported results of our baseline models in Tables 1, 2, and 3. The best-performing MuLT and FMT models are extremely dense, with around 15 and 77 million parameters, respectively. In contrast, our models have between 7-9 million trainable parameters, depending on the architecture; despite using about half as many parameters as MuLT, we see that our models produce comparable results.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Discussion",
"sec_num": "5"
},
{
"text": "We perform fairly well on IEMOCAP, which has around 2717 training samples; we achieve scores around 1-2% below the best-performing model, FMT. On the tiny CMU-MOSI dataset, which has just 1284 training samples, our Hybrid Fusion and Late Fusion models give state of the art results on seven-way and binary accuracy, respectively. The CMU-MOSEI dataset is much larger than IEMOCAP and CMU-MOSI, with close to 16265 training samples. Our models perform the weakest on this dataset, falling short of the state of the art models by around 2-3%, suggesting that our models may be too small to learn the entire distribution. Neither MARN [Zadeh et al., 2018c] nor FMT reports results on CMU-MOSEI, so they are omitted from Table 3 .",
"cite_spans": [
{
"start": 632,
"end": 653,
"text": "[Zadeh et al., 2018c]",
"ref_id": null
}
],
"ref_spans": [
{
"start": 717,
"end": 724,
"text": "Table 3",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Results and Discussion",
"sec_num": "5"
},
{
"text": "We also experiment with the open source code available for MuLT and FMT (denoted by *). Using the hyperparameter settings provided 2 , we were nevertheless unable to match those systems' reported performance, possibly due to differences 2 Batch size for FMT* is not given; we use 20, the default. resulting from random initialization. In training MuLT* and FMT*, we observe that the models are overfitting, with a mean difference of 15-20% between the train and test accuracy; in contrast, the largest train-test accuracy difference among our three models is only about 10%. The smaller number of parameters in our model reduces the risk of overfitting on smaller datasets, while still achieving good performance on larger datasets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results and Discussion",
"sec_num": "5"
},
{
"text": "We compare the training time and memory footprint of our models with MuLT* and FMT* in Table 4 3 . All models are trained on a single NVIDIA K80 GPU with 24GB of memory. We train for 30 epochs on IEMOCAP, 100 on CMU-MOSI and 40 on CMU-MOSEI (the number of epochs needed for MuLT to converge, as reported by Tsai et al. [2019] ). On the smallest dataset, CMU-MOSI, training MuLT* took just over seven minutes, while FMT* took 2.5 hours. Our models train in under three minutes and outperform both MuLT* and FMT*, and this difference in training speed holds for CMU-MOSI and CMU-MOSEI as well. Thus our model, available in the supplementary materials 4 , is the fastest and best-performing multimodal sentiment system currently available for public use.",
"cite_spans": [
{
"start": 307,
"end": 325,
"text": "Tsai et al. [2019]",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Analysis of Lightweight Architectures",
"sec_num": "5.1"
},
{
"text": "We also conduct experiments on a substantially reduced IEMOCAP training subset of 1284 samples, matching the size of CMU-MOSI, which we create by randomly sampling from the full IEMO-CAP training set. Table 5 shows the results of our models, as well as MuLT* and FMT*, retrained on this smaller IEMOCAP training set, and evaluated on the full IEMOCAP test set. We see that our models, with their smaller numbers of parameters, are better able to learn from limited training data than are state-of-the-art models with double or more the number of trainable parameters.",
"cite_spans": [],
"ref_spans": [
{
"start": 201,
"end": 208,
"text": "Table 5",
"ref_id": "TABREF6"
}
],
"eq_spans": [],
"section": "Analysis of Lightweight Architectures",
"sec_num": "5.1"
},
{
"text": "We perform ablation experiments on our models using the IEMOCAP dataset; ablation results for CMU-MOSI and CMU-MOSEI are omitted due to space constraints, but exhibit similar trends. Table 6 presents the results of modality ablation on the simplest Late Fusion model; it clearly shows that unimodal and bimodal models are unable to match the performance of a full multimodal model. This demonstrates the importance of considering all modalities when analyzing spoken language, since some of the emotions or sentiment may be dependent more on the audio or the visual actions of the speaker, rather than the text.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Analysis of Architecture Components",
"sec_num": "5.2"
},
{
"text": "Examining the unimodal results, we see that the Text modality is the most informative for predicting Happy, Sad, and Neutral, while Audio is the most informative for Angry. However, the bimodal results do not always match the unimodal results. The best-performing bimodal model for Happy is [V,A], despite Video being the worst-performing single modality, and [T,A] is the worst-performing bimodal model, despite both Text and Audio outperforming Video individually. Considering the other three emotions, we see that the best bimodal model varies between [T,A] and [V,A], with [T,V] generally performing the worst. Table 7 shows the results of modality ablation on the Round Robin model; as the architecture does not support unimodal experiments, only bimodal results are shown. Comparing Table 6 to Table 7 , we see that the cross-modal Transformers of the full Round Robin model are outperformed by the full Late Fusion model. However, the relative performance among modality pairs is consistent across Tables 6 and 7 .",
"cite_spans": [
{
"start": 577,
"end": 582,
"text": "[T,V]",
"ref_id": null
}
],
"ref_spans": [
{
"start": 615,
"end": 622,
"text": "Table 7",
"ref_id": "TABREF8"
},
{
"start": 789,
"end": 796,
"text": "Table 6",
"ref_id": "TABREF7"
},
{
"start": 800,
"end": 807,
"text": "Table 7",
"ref_id": "TABREF8"
},
{
"start": 1005,
"end": 1019,
"text": "Tables 6 and 7",
"ref_id": "TABREF7"
}
],
"eq_spans": [],
"section": "Analysis of Architecture Components",
"sec_num": "5.2"
},
{
"text": "Finally, Table 8 shows the results of modality ablation on the Hybrid Fusion model, where we compare the relative contributions of the early fusion and late fusion halves of the architecture. The top of the table shows the results of reducing the early fusion half to only two modalities while retaining all three modalities in the late fusion half, and the bottom shows the results of reducing the late fusion half to two modalities while retaining all three in the early fusion half; in both sets of experiments, the overall model has access to all three modalities, but only through either the early fusion path or the late fusion path.",
"cite_spans": [],
"ref_spans": [
{
"start": 9,
"end": 16,
"text": "Table 8",
"ref_id": "TABREF10"
}
],
"eq_spans": [],
"section": "Analysis of Architecture Components",
"sec_num": "5.2"
},
{
"text": "Surprisingly, although standalone early fusion models are outperformed by standalone late fusion models [Tsai et al., 2019] , we find that a hybrid model containing a full, trimodal early fusion half is more robust to modality ablation in its late fusion half than a model with a full late fusion half is to an ablated early fusion half. Our results in this experiment also show greater variability among modality pairs. The [T,A] combination, which gave the best performance in the Late Fusion and Round Robin experiments, remains the strongest modality pair for the full early fusion, bimodal late fusion model. In contrast, for the bimodal early fusion, full late fusion model, [T,A] is outperformed by one of the two Video-based modality pairs, [T,V] or [V,A], on each of the four emotions, suggesting that the performance gap of early versus late fusion differs across modalities.",
"cite_spans": [
{
"start": 104,
"end": 123,
"text": "[Tsai et al., 2019]",
"ref_id": "BIBREF15"
},
{
"start": 749,
"end": 754,
"text": "[T,V]",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Analysis of Architecture Components",
"sec_num": "5.2"
},
{
"text": "The effect of direction on our Round Robin model is shown in Table 9 ; this experiment shows the impact of the direction of information flow across modalities within the model. Comparing our results to those of MuLT and MuLT*, we see that capturing information flow in one direction, text to audio to video and back to text, is enough for a model to give good predictions, without requiring the additional overhead of handling both directions. We can also see that the direction does matter; the performance of the Round Robin model with information flowing in the opposite direction, from video to audio to text and back to video, is relatively poor. These results suggest that the interactions between pairs of modalities are directed.",
"cite_spans": [],
"ref_spans": [
{
"start": 61,
"end": 68,
"text": "Table 9",
"ref_id": "TABREF11"
}
],
"eq_spans": [],
"section": "Order of Modalities in Round Robin",
"sec_num": "5.2.1"
},
{
"text": "We have presented three lightweight architectures for multimodal sentiment analysis and emotion recognition. The Late Fusion model merges unimodal features, the Round Robin model iteratively combines bimodal features, and the Hybrid Early-Late Fusion model combines early-fusion trimodal and late-fusion unimodal features. Our proposed models are much smaller in size compared to existing state-of-the-art models; they are able to attain new state-of-the-art scores on the CMU-MOSI and CMU-MOSEI datasets on two metrics, while remaining competitive on the others. Further, our experiments analyzing the relative contribution of modalities and architecture components in our models suggest new directions for developing multimodal systems. We hope that our simple architectures for sentiment and emotion detection, currently the fastest and best-performing publicly available system, as well as the insights revealed in our experimental results, can be useful for further research in the field.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "We use the aligned version of MuLT for fair comparison with models that obligatorily use word alignments.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "FMT* does not provide hyperparameter settings for CMU-MOSEI, so those results are omitted.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "We will release it online after the anonymity period.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Deep canonical correlation analysis",
"authors": [
{
"first": "G",
"middle": [],
"last": "Andrew",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Arora",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Bilmes",
"suffix": ""
},
{
"first": "Karen",
"middle": [],
"last": "Livescu",
"suffix": ""
}
],
"year": 2013,
"venue": "ICML",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "G. Andrew, R. Arora, J. Bilmes, and Karen Livescu. Deep canonical correlation analysis. In ICML, 2013.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Openface 2.0: Facial behavior analysis toolkit",
"authors": [
{
"first": "Tadas",
"middle": [],
"last": "Baltrusaitis",
"suffix": ""
},
{
"first": "Amir",
"middle": [],
"last": "Zadeh",
"suffix": ""
},
{
"first": "Chong",
"middle": [],
"last": "Yao",
"suffix": ""
},
{
"first": "Louis-Philippe",
"middle": [],
"last": "Lim",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Morency",
"suffix": ""
}
],
"year": 2018,
"venue": "13th IEEE International Conference on Automatic Face Gesture Recognition (FG 2018)",
"volume": "",
"issue": "",
"pages": "59--66",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tadas Baltrusaitis, Amir Zadeh, Yao Chong Lim, and Louis-Philippe Morency. Openface 2.0: Facial behavior analysis toolkit. 2018 13th IEEE Inter- national Conference on Automatic Face Gesture Recognition (FG 2018), pages 59-66, 2018.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Iemocap: interactive emotional dyadic motion capture database",
"authors": [
{
"first": "Carlos",
"middle": [],
"last": "Busso",
"suffix": ""
},
{
"first": "Murtaza",
"middle": [],
"last": "Bulut",
"suffix": ""
},
{
"first": "Chi-Chun",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Abe",
"middle": [],
"last": "Kazemzadeh",
"suffix": ""
},
{
"first": "Emily",
"middle": [
"Mower"
],
"last": "Provost",
"suffix": ""
},
{
"first": "Samuel",
"middle": [],
"last": "Kim",
"suffix": ""
},
{
"first": "Jeannette",
"middle": [
"N"
],
"last": "Chang",
"suffix": ""
},
{
"first": "Sungbok",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Shrikanth",
"middle": [
"S"
],
"last": "Narayanan",
"suffix": ""
}
],
"year": 2008,
"venue": "Language Resources and Evaluation",
"volume": "42",
"issue": "",
"pages": "335--359",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower Provost, Samuel Kim, Jeannette N. Chang, Sungbok Lee, and Shrikanth S. Narayanan. Iemocap: interactive emotional dyadic motion capture database. Language Resources and Evaluation, 42:335-359, 2008.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "The best of both worlds: Combining recent advances in neural machine translation",
"authors": [
{
"first": "Mia",
"middle": [],
"last": "Xu Chen",
"suffix": ""
},
{
"first": "Orhan",
"middle": [],
"last": "Firat",
"suffix": ""
},
{
"first": "Ankur",
"middle": [],
"last": "Bapna",
"suffix": ""
},
{
"first": "Melvin",
"middle": [],
"last": "Johnson",
"suffix": ""
},
{
"first": "Wolfgang",
"middle": [],
"last": "Macherey",
"suffix": ""
},
{
"first": "George",
"middle": [],
"last": "Foster",
"suffix": ""
},
{
"first": "Llion",
"middle": [],
"last": "Jones",
"suffix": ""
},
{
"first": "Niki",
"middle": [],
"last": "Parmar",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Schuster",
"suffix": ""
},
{
"first": "Zhi-Feng",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Yonghui",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Macduff",
"middle": [],
"last": "Hughes",
"suffix": ""
}
],
"year": 2018,
"venue": "ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mia Xu Chen, Orhan Firat, Ankur Bapna, Melvin Johnson, Wolfgang Macherey, George Foster, Llion Jones, Niki Parmar, Michael Schuster, Zhi-Feng Chen, Yonghui Wu, and Macduff Hughes. The best of both worlds: Combining recent advances in neu- ral machine translation. In ACL, 2018.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Covarep -a collaborative voice analysis repository for speech technologies",
"authors": [
{
"first": "Gilles",
"middle": [],
"last": "Degottex",
"suffix": ""
},
{
"first": "John",
"middle": [],
"last": "Kane",
"suffix": ""
},
{
"first": "Thomas",
"middle": [],
"last": "Drugman",
"suffix": ""
},
{
"first": "Tuomo",
"middle": [],
"last": "Raitio",
"suffix": ""
},
{
"first": "Stefan",
"middle": [],
"last": "Scherer",
"suffix": ""
}
],
"year": 2014,
"venue": "IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",
"volume": "",
"issue": "",
"pages": "960--964",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gilles Degottex, John Kane, Thomas Drugman, Tuomo Raitio, and Stefan Scherer. Covarep -a collabo- rative voice analysis repository for speech technolo- gies. 2014 IEEE International Conference on Acous- tics, Speech and Signal Processing (ICASSP), pages 960-964, 2014.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"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": "ArXiv",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidi- rectional transformers for language understanding. ArXiv, abs/1810.04805, 2019.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Understanding back-translation at scale",
"authors": [
{
"first": "Sergey",
"middle": [],
"last": "Edunov",
"suffix": ""
},
{
"first": "Myle",
"middle": [],
"last": "Ott",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Auli",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Grangier",
"suffix": ""
}
],
"year": 2018,
"venue": "In EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sergey Edunov, Myle Ott, Michael Auli, and David Grangier. Understanding back-translation at scale. In EMNLP, 2018.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Facial expression analysis",
"authors": [],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "iMotions. Facial expression analysis, 2017. URL https://rb.gy/hkrcc4.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Multimodal language analysis with recurrent multistage fusion",
"authors": [
{
"first": "Ziyin",
"middle": [],
"last": "Paul Pu Liang",
"suffix": ""
},
{
"first": "Amir",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Louis-Philippe",
"middle": [],
"last": "Zadeh",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Morency",
"suffix": ""
}
],
"year": 2018,
"venue": "EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Paul Pu Liang, Ziyin Liu, Amir Zadeh, and Louis- Philippe Morency. Multimodal language analysis with recurrent multistage fusion. EMNLP, 2018.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Efficient low-rank multimodal fusion with modality-specific factors",
"authors": [
{
"first": "Zhun",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Ying",
"middle": [],
"last": "Shen",
"suffix": ""
},
{
"first": "Varun",
"middle": [],
"last": "Bharadhwaj Lakshminarasimhan",
"suffix": ""
},
{
"first": "Paul",
"middle": [
"Pu"
],
"last": "Liang",
"suffix": ""
},
{
"first": "Amir",
"middle": [],
"last": "Zadeh",
"suffix": ""
},
{
"first": "Louis-Philippe",
"middle": [],
"last": "Morency",
"suffix": ""
}
],
"year": 2018,
"venue": "ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshmi- narasimhan, Paul Pu Liang, Amir Zadeh, and Louis- Philippe Morency. Efficient low-rank multimodal fusion with modality-specific factors. In ACL, 2018.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Neurospeech: An open-source software for parkinson's speech analysis",
"authors": [
{
"first": "R",
"middle": [],
"last": "Juan",
"suffix": ""
},
{
"first": "Juan",
"middle": [
"Camilo"
],
"last": "Orozco-Arroyave",
"suffix": ""
},
{
"first": "Jesus",
"middle": [
"Francisco"
],
"last": "Vasquez-Correa",
"suffix": ""
},
{
"first": "Raman",
"middle": [],
"last": "Vargas-Bonilla",
"suffix": ""
},
{
"first": "Najim",
"middle": [],
"last": "Arora",
"suffix": ""
},
{
"first": "Phani",
"middle": [
"S"
],
"last": "Dehak",
"suffix": ""
},
{
"first": "Heidi",
"middle": [],
"last": "Nidadavolu",
"suffix": ""
},
{
"first": "Frank",
"middle": [],
"last": "Christensen",
"suffix": ""
},
{
"first": "Maria",
"middle": [],
"last": "Rudzicz",
"suffix": ""
},
{
"first": "Hamid",
"middle": [
"R"
],
"last": "Yancheva",
"suffix": ""
},
{
"first": "Alyssa",
"middle": [],
"last": "Chinaei",
"suffix": ""
},
{
"first": "Nikolai",
"middle": [],
"last": "Vann",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Vogler",
"suffix": ""
}
],
"year": 2018,
"venue": "Tobias Bocklet, Milos Cernak, Julius Hannink, and Elmar Noth",
"volume": "77",
"issue": "",
"pages": "207--221",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Juan R. Orozco-Arroyave, Juan Camilo Vasquez- Correa, Jesus Francisco Vargas-Bonilla, Raman Arora, Najim Dehak, Phani S. Nidadavolu, Heidi Christensen, Frank Rudzicz, Maria Yancheva, Hamid R. Chinaei, Alyssa Vann, Nikolai Vogler, To- bias Bocklet, Milos Cernak, Julius Hannink, and El- mar Noth. Neurospeech: An open-source software for parkinson's speech analysis. Digital Signal Pro- cess., 77:207-221, 2018.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Glove: Global vectors for word representation",
"authors": [
{
"first": "Jeffrey",
"middle": [],
"last": "Pennington",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Socher",
"suffix": ""
},
{
"first": "Christopher",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
}
],
"year": 2014,
"venue": "EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jeffrey Pennington, Richard Socher, and Christopher D. Manning. Glove: Global vectors for word represen- tation. In EMNLP, 2014.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Found in translation: Learning robust joint representations by cyclic translations between modalities",
"authors": [
{
"first": "Hai",
"middle": [],
"last": "Pham",
"suffix": ""
},
{
"first": "Paul",
"middle": [
"Pu"
],
"last": "Liang",
"suffix": ""
},
{
"first": "Thomas",
"middle": [],
"last": "Manzini",
"suffix": ""
}
],
"year": 2019,
"venue": "AAAI",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hai Pham, Paul Pu Liang, Thomas Manzini, Louis- Philippe Morency, and Barnabas Poczos. Found in translation: Learning robust joint representations by cyclic translations between modalities. In AAAI, 2019.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Look, listen, and decode: Multimodal speech recognition with images",
"authors": [
{
"first": "Felix",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "David",
"middle": [
"F"
],
"last": "Harwath",
"suffix": ""
},
{
"first": "James",
"middle": [
"R"
],
"last": "Glass",
"suffix": ""
}
],
"year": 2016,
"venue": "IEEE Spoken Language Technology Workshop (SLT)",
"volume": "",
"issue": "",
"pages": "573--578",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Felix Sun, David F. Harwath, and James R. Glass. Look, listen, and decode: Multimodal speech recog- nition with images. 2016 IEEE Spoken Language Technology Workshop (SLT), pages 573-578, 2016.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Learning relationships between text, audio, and video via deep canonical correlation for multimodal language analysis",
"authors": [
{
"first": "Zhongkai",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Sarma",
"suffix": ""
},
{
"first": "W",
"middle": [],
"last": "Sethares",
"suffix": ""
},
{
"first": "Yingyu",
"middle": [],
"last": "Liang",
"suffix": ""
}
],
"year": 2020,
"venue": "ArXiv",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhongkai Sun, P. Sarma, W. Sethares, and Yingyu Liang. Learning relationships between text, audio, and video via deep canonical correlation for mul- timodal language analysis. ArXiv, abs/1911.05544, 2020.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Multimodal transformer for unaligned multimodal language sequences",
"authors": [
{
"first": "Yao-Hung Hubert",
"middle": [],
"last": "Tsai",
"suffix": ""
},
{
"first": "Shaojie",
"middle": [],
"last": "Bai",
"suffix": ""
},
{
"first": "Paul",
"middle": [
"Pu"
],
"last": "Liang",
"suffix": ""
},
{
"first": "J",
"middle": [
"Zico"
],
"last": "Kolter",
"suffix": ""
},
{
"first": "Louis-Philippe",
"middle": [],
"last": "Morency",
"suffix": ""
},
{
"first": "Ruslan",
"middle": [],
"last": "Salakhutdinov",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the conference. Association for Computational Linguistics. Meeting",
"volume": "",
"issue": "",
"pages": "6558--6569",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, J. Zico Kolter, Louis-Philippe Morency, and Rus- lan Salakhutdinov. Multimodal transformer for un- aligned multimodal language sequences. Proceed- ings of the conference. Association for Computa- tional Linguistics. Meeting, 2019:6558-6569, 2019. version 2.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Neural machine translation with the transformer and multisource romance languages for the biomedical wmt 2018 task",
"authors": [
{
"first": "Brian",
"middle": [],
"last": "Tubay",
"suffix": ""
},
{
"first": "Marta",
"middle": [
"R"
],
"last": "Costa-Juss\u00e0",
"suffix": ""
}
],
"year": 2018,
"venue": "WMT",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Brian Tubay and Marta R. Costa-juss\u00e0. Neural ma- chine translation with the transformer and multi- source romance languages for the biomedical wmt 2018 task. In WMT, 2018.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Attention is all you need",
"authors": [
{
"first": "Ashish",
"middle": [],
"last": "Vaswani",
"suffix": ""
},
{
"first": "Noam",
"middle": [],
"last": "Shazeer",
"suffix": ""
},
{
"first": "Niki",
"middle": [],
"last": "Parmar",
"suffix": ""
},
{
"first": "Jakob",
"middle": [],
"last": "Uszkoreit",
"suffix": ""
},
{
"first": "Llion",
"middle": [],
"last": "Jones",
"suffix": ""
},
{
"first": "Aidan",
"middle": [
"N"
],
"last": "Gomez",
"suffix": ""
},
{
"first": "\u0141ukasz",
"middle": [],
"last": "Kaiser",
"suffix": ""
},
{
"first": "Illia",
"middle": [],
"last": "Polosukhin",
"suffix": ""
}
],
"year": 2017,
"venue": "Advances in neural information processing systems",
"volume": "",
"issue": "",
"pages": "5998--6008",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, \u0141ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in neural information processing systems, pages 5998-6008, 2017.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Words can shift: Dynamically adjusting word representations using nonverbal behaviors",
"authors": [
{
"first": "Yansen",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Ying",
"middle": [],
"last": "Shen",
"suffix": ""
},
{
"first": "Zhun",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Paul",
"middle": [
"Pu"
],
"last": "Liang",
"suffix": ""
},
{
"first": "Amir",
"middle": [],
"last": "Zadeh",
"suffix": ""
},
{
"first": "Louis-Philippe",
"middle": [],
"last": "Morency",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence",
"volume": "33",
"issue": "",
"pages": "7216--7223",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yansen Wang, Ying Shen, Zhun Liu, Paul Pu Liang, Amir Zadeh, and Louis-Philippe Morency. Words can shift: Dynamically adjusting word representa- tions using nonverbal behaviors. Proceedings of the AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 33 1:7216- 7223, 2019.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Tied transformers: Neural machine translation with shared encoder and decoder",
"authors": [
{
"first": "Yingce",
"middle": [],
"last": "Xia",
"suffix": ""
},
{
"first": "Tianyu",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Xu",
"middle": [],
"last": "Tan",
"suffix": ""
},
{
"first": "Fei",
"middle": [],
"last": "Tian",
"suffix": ""
},
{
"first": "Di",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Tao",
"middle": [],
"last": "Qin",
"suffix": ""
}
],
"year": 2019,
"venue": "AAAI",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yingce Xia, Tianyu He, Xu Tan, Fei Tian, Di He, and Tao Qin. Tied transformers: Neural machine trans- lation with shared encoder and decoder. In AAAI, 2019.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Aggregated residual transformations for deep neural networks",
"authors": [
{
"first": "Saining",
"middle": [],
"last": "Xie",
"suffix": ""
},
{
"first": "Ross",
"middle": [
"B"
],
"last": "Girshick",
"suffix": ""
},
{
"first": "Piotr",
"middle": [],
"last": "Doll\u00e1r",
"suffix": ""
},
{
"first": "Zhuowen",
"middle": [],
"last": "Tu",
"suffix": ""
},
{
"first": "Kaiming",
"middle": [],
"last": "He",
"suffix": ""
}
],
"year": 2017,
"venue": "IEEE Conference on Computer Vision and Pattern Recognition (CVPR)",
"volume": "",
"issue": "",
"pages": "5987--5995",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Saining Xie, Ross B. Girshick, Piotr Doll\u00e1r, Zhuowen Tu, and Kaiming He. Aggregated residual transfor- mations for deep neural networks. 2017 IEEE Con- ference on Computer Vision and Pattern Recognition (CVPR), pages 5987-5995, 2017.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Multimodal sentiment intensity analysis in videos: Facial gestures and verbal messages",
"authors": [
{
"first": "Amir",
"middle": [],
"last": "Zadeh",
"suffix": ""
},
{
"first": "Rowan",
"middle": [],
"last": "Zellers",
"suffix": ""
},
{
"first": "Eli",
"middle": [],
"last": "Pincus",
"suffix": ""
},
{
"first": "Louis-Philippe",
"middle": [],
"last": "Morency",
"suffix": ""
}
],
"year": 2016,
"venue": "IEEE Intelligent Systems",
"volume": "31",
"issue": "",
"pages": "82--88",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Amir Zadeh, Rowan Zellers, Eli Pincus, and Louis- Philippe Morency. Multimodal sentiment intensity analysis in videos: Facial gestures and verbal mes- sages. IEEE Intelligent Systems, 31:82-88, 2016.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. Memory fusion network for multi-view sequential learning",
"authors": [
{
"first": "Amir",
"middle": [],
"last": "Zadeh",
"suffix": ""
},
{
"first": "Paul",
"middle": [
"Pu"
],
"last": "Liang",
"suffix": ""
},
{
"first": "Navonil",
"middle": [],
"last": "Mazumder",
"suffix": ""
}
],
"year": 2018,
"venue": "AAAI",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Amir Zadeh, Paul Pu Liang, Navonil Mazumder, Soujanya Poria, Erik Cambria, and Louis-Philippe Morency. Memory fusion network for multi-view sequential learning. In AAAI, 2018a. version 1.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Erik Cambria, and Louis-Philippe Morency. Multimodal language analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph",
"authors": [
{
"first": "Amir",
"middle": [],
"last": "Zadeh",
"suffix": ""
},
{
"first": "Paul",
"middle": [
"Pu"
],
"last": "Liang",
"suffix": ""
},
{
"first": "Soujanya",
"middle": [],
"last": "Poria",
"suffix": ""
}
],
"year": 2018,
"venue": "ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Amir Zadeh, Paul Pu Liang, Soujanya Poria, Erik Cam- bria, and Louis-Philippe Morency. Multimodal lan- guage analysis in the wild: Cmu-mosei dataset and interpretable dynamic fusion graph. In ACL, 2018b.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Prateek Vij, Erik Cambria, and Louis-Philippe Morency. Multi-attention recurrent network for human communication comprehension",
"authors": [
{
"first": "Amir",
"middle": [],
"last": "Zadeh",
"suffix": ""
},
{
"first": "Paul",
"middle": [
"Pu"
],
"last": "Liang",
"suffix": ""
},
{
"first": "Soujanya",
"middle": [],
"last": "Poria",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Amir Zadeh, Paul Pu Liang, Soujanya Poria, Pra- teek Vij, Erik Cambria, and Louis-Philippe Morency. Multi-attention recurrent network for human com- munication comprehension. Proceedings of the ...",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence",
"authors": [],
"year": 2018,
"venue": "",
"volume": "2018",
"issue": "",
"pages": "5642--5649",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, 2018:5642- 5649, 2018c. version 2.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Factorized multimodal transformer for multimodal sequential learning. ArXiv, abs",
"authors": [
{
"first": "Amir",
"middle": [],
"last": "Zadeh",
"suffix": ""
},
{
"first": "Chengfeng",
"middle": [],
"last": "Mao",
"suffix": ""
},
{
"first": "Kelly",
"middle": [],
"last": "Shi",
"suffix": ""
},
{
"first": "Yiwei",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Paul",
"middle": [
"Pu"
],
"last": "Liang",
"suffix": ""
},
{
"first": "Soujanya",
"middle": [],
"last": "Poria",
"suffix": ""
},
{
"first": "Louis-Philippe",
"middle": [],
"last": "Morency",
"suffix": ""
}
],
"year": 1911,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Amir Zadeh, Chengfeng Mao, Kelly Shi, Yiwei Zhang, Paul Pu Liang, Soujanya Poria, and Louis- Philippe Morency. Factorized multimodal trans- former for multimodal sequential learning. ArXiv, abs/1911.09826, 2019.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"num": null,
"text": "Architecture of our Late Fusion model. Unimodal Transformers process each modality separately; the outputs of these Transformers are summed and passed through a residual network of linear layers to produce the final prediction.",
"type_str": "figure",
"uris": null
},
"FIGREF1": {
"num": null,
"text": "Architecture of our Round Robin model. Modalities are combined in a round-robin fashion via thrree cross-modal Transformers, one for each ordered pair of modalities: [T, A], [A, V ], [V, T ]. The outputs of these cross-modal Transformers are concatenated and passed through a residual network of linear layers to produce the final prediction.",
"type_str": "figure",
"uris": null
},
"FIGREF2": {
"num": null,
"text": "Architecture of our Hybrid Fusion model. All three modalities are passed through an early fusion Transformer to produce trimodal features; in parallel, they are individually passed to separate Transformers to produce unimodal features. All features are then concatenated and passed through a GRU and a residual network of linear layers to produce the final prediction.",
"type_str": "figure",
"uris": null
},
"TABREF1": {
"type_str": "table",
"num": null,
"text": "Emotion recognition results on IEMOCAP. The metrics are binary (one vs all) accuracy and the F1 score for each of the four emotions. * indicates results from open source code. Bold indicates scores higher than that of our model.",
"html": null,
"content": "<table><tr><td>Model</td><td colspan=\"3\">ACC7 ACC2 F1</td><td colspan=\"2\">MAE Corr</td></tr><tr><td>MARN</td><td>-</td><td>77.1</td><td>77.0</td><td>0.97</td><td>0.63</td></tr><tr><td>MFN</td><td>-</td><td>77.4</td><td>77.3</td><td>0.97</td><td>0.63</td></tr><tr><td>RAVEN</td><td>33.2</td><td>78.0</td><td>76.6</td><td>0.92</td><td>0.69</td></tr><tr><td>MCTN</td><td>35.6</td><td>79.3</td><td>79.1</td><td>0.91</td><td>0.68</td></tr><tr><td>ICCN</td><td>39.0</td><td>83.1</td><td>83.0</td><td>0.86</td><td>0.71</td></tr><tr><td>MuLT</td><td>40.0</td><td>83.0</td><td>82.8</td><td>0.87</td><td>0.70</td></tr><tr><td>MuLT*</td><td>30.7</td><td>77.5</td><td>76.9</td><td>1.04</td><td>0.66</td></tr><tr><td>FMT</td><td>-</td><td>83.5</td><td>83.5</td><td>0.84</td><td>0.74</td></tr><tr><td>FMT*</td><td>-</td><td>78.3</td><td>77.8</td><td>0.91</td><td>0.70</td></tr><tr><td>Late Fusion</td><td>40.2</td><td>83.6</td><td>80.0</td><td>0.92</td><td>0.69</td></tr><tr><td>Round Robin</td><td>39.3</td><td>78.1</td><td>76.7</td><td>0.96</td><td>0.68</td></tr><tr><td colspan=\"2\">Hybrid Fusion 40.6</td><td>82.1</td><td>79.9</td><td>0.94</td><td>0.69</td></tr></table>"
},
"TABREF2": {
"type_str": "table",
"num": null,
"text": "Sentiment analysis results on CMU-MOSI. ACC7",
"html": null,
"content": "<table><tr><td colspan=\"6\">was not reported by some baselines. The metrics are seven-</td></tr><tr><td colspan=\"6\">way and binary accuracy, F1 score, mean absolute error, and</td></tr><tr><td colspan=\"6\">correlation with human judgments. All metrics are better</td></tr><tr><td colspan=\"4\">when higher, except for mean absolute error.</td><td/><td/></tr><tr><td>Model</td><td colspan=\"3\">ACC7 ACC2 F1</td><td colspan=\"2\">MAE Corr</td></tr><tr><td>MFN</td><td>45.0</td><td>76.9</td><td>77.0</td><td>0.71</td><td>0.54</td></tr><tr><td>RAVEN</td><td>50.0</td><td>79.1</td><td>79.5</td><td>0.61</td><td>0.66</td></tr><tr><td>MCTN</td><td>49.6</td><td>79.8</td><td>80.6</td><td>0.61</td><td>0.67</td></tr><tr><td>ICCN</td><td>51.6</td><td>84.2</td><td>84.2</td><td>0.57</td><td>0.71</td></tr><tr><td>MuLT</td><td>51.8</td><td>82.5</td><td>82.3</td><td>0.58</td><td>0.70</td></tr><tr><td>MuLT*</td><td>48.9</td><td>80.7</td><td>80.9</td><td>0.63</td><td>0.65</td></tr><tr><td>Late Fusion</td><td>52.3</td><td>80.7</td><td>80.7</td><td>0.61</td><td>0.69</td></tr><tr><td>Round Robin</td><td>51.4</td><td>80.6</td><td>79.9</td><td>0.62</td><td>0.66</td></tr><tr><td colspan=\"2\">Hybrid Fusion 51.9</td><td>80.6</td><td>80.5</td><td>0.61</td><td>0.68</td></tr></table>"
},
"TABREF3": {
"type_str": "table",
"num": null,
"text": "Sentiment analysis results on CMU-MOSEI. The metrics used are the same as inTable 2.",
"html": null,
"content": "<table/>"
},
"TABREF5": {
"type_str": "table",
"num": null,
"text": "Comparison of training time and memory use among MuLT*, FMT*, and our models.",
"html": null,
"content": "<table><tr><td>Model</td><td/><td>Happy</td><td/><td>Sad</td><td/><td>Angry</td><td/><td>Neutral</td></tr><tr><td>Metric</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td></tr><tr><td>MuLT*</td><td>82.6</td><td>81.5</td><td>79.4</td><td>80.7</td><td>78.3</td><td>78.9</td><td>60.1</td><td>60.7</td></tr><tr><td>FMT*</td><td>82.1</td><td>81.2</td><td>80.2</td><td>80.9</td><td>80.0</td><td>81.7</td><td>60.5</td><td>60.2</td></tr><tr><td>Late Fusion</td><td>84.1</td><td>82.4</td><td>80.3</td><td>76.5</td><td>81.0</td><td>79.4</td><td>61.6</td><td>61.2</td></tr><tr><td>Round Robin</td><td>85.2</td><td>81.2</td><td>79.9</td><td>77.2</td><td>79.0</td><td>76.6</td><td>63.2</td><td>58.1</td></tr><tr><td colspan=\"2\">Hybrid Fusion 85.5</td><td>80.7</td><td>80.8</td><td>79.9</td><td>81.0</td><td>80.8</td><td>64.7</td><td>63.5</td></tr></table>"
},
"TABREF6": {
"type_str": "table",
"num": null,
"text": "Results on the reduced IEMOCAP dataset of 1284 training samples. The metrics used are the same as inTable 1.",
"html": null,
"content": "<table><tr><td>Model</td><td/><td>Happy</td><td/><td>Sad</td><td/><td>Angry</td><td/><td>Neutral</td></tr><tr><td>Metric</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td></tr><tr><td>Unimodal [T]</td><td>86.4</td><td>84.0</td><td>82.7</td><td>78.5</td><td>81.6</td><td>78.3</td><td>67.9</td><td>65.9</td></tr><tr><td>Unimodal [A]</td><td>85.9</td><td>79.0</td><td>82.2</td><td>81.5</td><td>85.9</td><td>85.9</td><td>62.8</td><td>60.5</td></tr><tr><td>Unimodal [V]</td><td>85.1</td><td>81.0</td><td>79.1</td><td>70.4</td><td>75.6</td><td>74.1</td><td>58.8</td><td>56.3</td></tr><tr><td>Bimodal [T,A]</td><td>84.5</td><td>82.6</td><td>84.8</td><td>84.1</td><td>85.8</td><td>86.1</td><td>68.9</td><td>67.2</td></tr><tr><td>Bimodal [T,V]</td><td>85.3</td><td>85.1</td><td>80.1</td><td>80.7</td><td>84.2</td><td>83.5</td><td>66.4</td><td>65.4</td></tr><tr><td>Bimodal [V,A]</td><td>86.8</td><td>82.9</td><td>81.4</td><td>77.9</td><td>86.4</td><td>86.1</td><td>62.5</td><td>62.6</td></tr><tr><td colspan=\"2\">Late Fusion [T,A,V] 87.7</td><td>86.8</td><td>87.3</td><td>86.8</td><td>87.9</td><td>87.0</td><td>72.0</td><td>71.5</td></tr></table>"
},
"TABREF7": {
"type_str": "table",
"num": null,
"text": "Ablation results on IEMOCAP for our Late Fusion model.",
"html": null,
"content": "<table><tr><td>Model</td><td/><td>Happy</td><td/><td>Sad</td><td/><td>Angry</td><td/><td>Neutral</td></tr><tr><td>Metric</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td></tr><tr><td>Bimodal [T,A]</td><td>85.2</td><td>82.9</td><td>82.9</td><td>83.9</td><td>86.2</td><td>86.4</td><td>70.2</td><td>69.5</td></tr><tr><td>Bimodal [T,V]</td><td>86.4</td><td>83.9</td><td>79.3</td><td>77.4</td><td>81.4</td><td>81.4</td><td>65.1</td><td>65.0</td></tr><tr><td>Bimodal [V,A]</td><td>86.4</td><td>82.5</td><td>79.6</td><td>78.6</td><td>85.6</td><td>85.2</td><td>63.1</td><td>62.7</td></tr><tr><td colspan=\"2\">Round Robin [T,A,V] 87.5</td><td>84.9</td><td>85.2</td><td>87.4</td><td>87.5</td><td>86.8</td><td>70.0</td><td>69.4</td></tr></table>"
},
"TABREF8": {
"type_str": "table",
"num": null,
"text": "Ablation results on IEMOCAP for our Round Robin model.",
"html": null,
"content": "<table/>"
},
"TABREF10": {
"type_str": "table",
"num": null,
"text": "Ablation results on IEMOCAP for our Hybrid Fusion model: bimodal early fusion with trimodal late fusion (top) and trimodal early fusion with bimodal late fusion (bottom).",
"html": null,
"content": "<table><tr><td>Model</td><td/><td>Happy</td><td/><td>Sad</td><td/><td>Angry</td><td/><td>Neutral</td></tr><tr><td>Metric</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td><td>BA</td><td>F1</td></tr><tr><td>MuLT</td><td>90.7</td><td>88.6</td><td>86.7</td><td>86.0</td><td>87.4</td><td>87.0</td><td>72.4</td><td>70.7</td></tr><tr><td>MuLT*</td><td>84.7</td><td>83.5</td><td>84.5</td><td>84.1</td><td>84.85</td><td>84.7</td><td>70.4</td><td>70.7</td></tr><tr><td colspan=\"2\">Round Robin [T \u2192 A \u2192 V] 87.5</td><td>84.9</td><td>85.2</td><td>84.4</td><td>87.4</td><td>87.5</td><td>70.0</td><td>69.4</td></tr><tr><td colspan=\"2\">Round Robin [V \u2192 A \u2192 T] 83.0</td><td>81.8</td><td>82.2</td><td>83.7</td><td>85.9</td><td>82.7</td><td>68.2</td><td>68.3</td></tr></table>"
},
"TABREF11": {
"type_str": "table",
"num": null,
"text": "Results on IEMOCAP for our Round Robin model, comparing information flow in each direction, alongside MuLT and MuLT*, which capture information flowing in both directions.",
"html": null,
"content": "<table/>"
}
}
}
} |