metadata

annotations_creators:
  - found
language_creators:
  - expert-generated
language:
  - cs
  - de
  - en
  - et
  - fi
  - fr
  - gu
  - iu
  - ja
  - kk
  - km
  - lt
  - lv
  - pl
  - ps
  - ro
  - ru
  - ta
  - tr
  - zh
license:
  - unknown
multilinguality:
  - multilingual
  - translation
size_categories:
  - unknown
source_datasets:
  - extended|wmt16
  - extended|wmt17
  - extended|wmt18
  - extended|wmt19
  - extended|wmt20
task_categories:
  - text-generation
  - translation
task_ids: []
pretty_name: wmt_vat
tags:
  - conditional-text-generation

Dataset Card for Variance-Aware MT Test Sets

Dataset Card for Variance-Aware MT Test Sets

Dataset Description

Repository: Github
Paper: NeurIPS
Point of Contact: Runzhe Zhan

Dataset Summary

This dataset comprises 70 small and discriminative test sets for machine translation (MT) evaluation called variance-aware test sets (VAT), covering 35 translation directions from WMT16 to WMT20 competitions. VAT is automatically created by a novel variance-aware filtering method that filters the indiscriminative test instances of the current MT benchmark without any human labor. Experimental results show that VAT outperforms the original WMT benchmark in terms of the correlation with human judgment across mainstream language pairs and test sets. Further analysis on the properties of VAT reveals the challenging linguistic features (e.g., translation of low-frequency words and proper nouns) for the competitive MT systems, providing guidance for constructing future MT test sets.

Disclaimer: *The VAT test sets are hosted through Github by the Natural Language Processing & Portuguese-Chinese Machine Translation Laboratory (NLP2CT Lab) of the University of Macau. They were introduced by the paper Variance-Aware Machine Translation Test Sets by Runzhe Zhan, Xuebo Liu, Derek F. Wong, Lidia S. Chao and follow the original licensing for WMT test sets.

Supported Tasks and Leaderboards

Machine Translation

Refer to the original paper for additional details on model evaluation on VAT.

Languages

The following table taken from the original paper lists the languages supported by the VAT test sets, for a total of 70 language pairs:

↔️	`wmt16`	`wmt17`	`wmt18`	`wmt19`	`wmt20`
`xx_en`	`cs`,`de`,`fi`, `ro`,`ru`,`tr`	`cs`,`de`,`fi`,`lv`, `ru`,`tr`,`zh`	`cs`,`de`,`et`,`fi`, `ru`,`tr`,`zh`	`de`,`fi`,`gu`, `kk`,`lt`,`ru`,`zh`	`cs`,`de`,`iu`,`ja`,`km`, `pl`,`ps`,`ru`,`ta`,`zh`
`en_xx`	`ru`	`cs`,`de`,`fi`, `lv`,`ru`,`tr`,`zh`	`cs`,`de`,`et`,`fi`, `ru`,`tr`,`zh`	`cs`,`de`,`fi`,`gu`, `kk`,`lt`,`ru`,`zh`	`cs`,`de`,`ja`,`pl`, `ru`,`ta`,`zh`
`xx_yy`	/	/	/	`de_cs`,`de_fr`, `fr_de`	/

To use any one of the test set, pass wmtXX_src_tgt as configuration name to the load_dataset command. E.g. to load the English-Russian test set from wmt16, use load_dataset('gsarti/wmt_vat', 'wmt16_en_ru').

Dataset Structure

Data Instances

A sample from the test split (the only available split) for the WMT16 English-Russian language (wmt16_en_ru config) is provided below. All configurations have the same structure.

{
    'orig_id': 0,
    'source': 'The social card of residents of Ivanovo region is to be recognised as an electronic payment instrument.',
    'reference': 'Социальная карта жителя Ивановской области признается электронным средством платежа.'
}

The text is provided as-in the original dataset, without further preprocessing or tokenization.

Data Fields

orig_id: Id corresponding to the row id in the original dataset, before variance-aware filtering.
source: The source sentence.
reference: The reference sentence in the target language.

Data Splits

Taken from the original repository:

Configuration	# Sentences	# Words	# Vocabulary
`wmt20_km_en`	928	17170	3645
`wmt20_cs_en`	266	12568	3502
`wmt20_en_de`	567	21336	5945
`wmt20_ja_en`	397	10526	3063
`wmt20_ps_en`	1088	20296	4303
`wmt20_en_zh`	567	18224	5019
`wmt20_en_ta`	400	7809	4028
`wmt20_de_en`	314	16083	4046
`wmt20_zh_en`	800	35132	6457
`wmt20_en_ja`	400	12718	2969
`wmt20_en_cs`	567	16579	6391
`wmt20_en_pl`	400	8423	3834
`wmt20_en_ru`	801	17446	6877
`wmt20_pl_en`	400	7394	2399
`wmt20_iu_en`	1188	23494	3876
`wmt20_ru_en`	396	6966	2330
`wmt20_ta_en`	399	7427	2148
`wmt19_zh_en`	800	36739	6168
`wmt19_en_cs`	799	15433	6111
`wmt19_de_en`	800	15219	4222
`wmt19_en_gu`	399	8494	3548
`wmt19_fr_de`	680	12616	3698
`wmt19_en_zh`	799	20230	5547
`wmt19_fi_en`	798	13759	3555
`wmt19_en_fi`	799	13303	6149
`wmt19_kk_en`	400	9283	2584
`wmt19_de_cs`	799	15080	6166
`wmt19_lt_en`	400	10474	2874
`wmt19_en_lt`	399	7251	3364
`wmt19_ru_en`	800	14693	3817
`wmt19_en_kk`	399	6411	3252
`wmt19_en_ru`	799	16393	6125
`wmt19_gu_en`	406	8061	2434
`wmt19_de_fr`	680	16181	3517
`wmt19_en_de`	799	18946	5340
`wmt18_en_cs`	1193	19552	7926
`wmt18_cs_en`	1193	23439	5453
`wmt18_en_fi`	1200	16239	7696
`wmt18_en_tr`	1200	19621	8613
`wmt18_en_et`	800	13034	6001
`wmt18_ru_en`	1200	26747	6045
`wmt18_et_en`	800	20045	5045
`wmt18_tr_en`	1200	25689	5955
`wmt18_fi_en`	1200	24912	5834
`wmt18_zh_en`	1592	42983	7985
`wmt18_en_zh`	1592	34796	8579
`wmt18_en_ru`	1200	22830	8679
`wmt18_de_en`	1199	28275	6487
`wmt18_en_de`	1199	25473	7130
`wmt17_en_lv`	800	14453	6161
`wmt17_zh_en`	800	20590	5149
`wmt17_en_tr`	1203	17612	7714
`wmt17_lv_en`	800	18653	4747
`wmt17_en_de`	1202	22055	6463
`wmt17_ru_en`	1200	24807	5790
`wmt17_en_fi`	1201	17284	7763
`wmt17_tr_en`	1203	23037	5387
`wmt17_en_zh`	800	18001	5629
`wmt17_en_ru`	1200	22251	8761
`wmt17_fi_en`	1201	23791	5300
`wmt17_en_cs`	1202	21278	8256
`wmt17_de_en`	1202	23838	5487
`wmt17_cs_en`	1202	22707	5310
`wmt16_tr_en`	1200	19225	4823
`wmt16_ru_en`	1199	23010	5442
`wmt16_ro_en`	800	16200	3968
`wmt16_de_en`	1200	22612	5511
`wmt16_en_ru`	1199	20233	7872
`wmt16_fi_en`	1200	20744	5176
`wmt16_cs_en`	1200	23235	5324

Dataset Creation

The dataset was created by retaining a subset of the top 40% instances from various WMT test sets for which the variance between automatic scores (BLEU, BLEURT, COMET, BERTScore) was the highest. Please refer to the original article Variance-Aware Machine Translation Test Sets for additional information on dataset creation.

Additional Information

Dataset Curators

The original authors of VAT are the curators of the original dataset. For problems or updates on this 🤗 Datasets version, please contact [email protected].

Licensing Information

The variance-aware test set were created based on the original WMT test set. Thus, the the original data licensing plan already stated by WMT organizers is still applicable:

The data released for the WMT news translation task can be freely used for research purposes, we just ask that you cite the WMT shared task overview paper, and respect any additional citation requirements on the individual data sets. For other uses of the data, you should consult with original owners of the data sets.

Citation Information

Please cite the authors if you use these corpora in your work. It is also advised to cite the original WMT shared task paper for the specific test sets that were used.

@inproceedings{
    zhan2021varianceaware,
    title={Variance-Aware Machine Translation Test Sets},
    author={Runzhe Zhan and Xuebo Liu and Derek F. Wong and Lidia S. Chao},
    booktitle={Thirty-fifth Conference on Neural Information Processing Systems, Datasets and Benchmarks Track},
    year={2021},
    url={https://openreview.net/forum?id=hhKA5k0oVy5}
}

Datasets:

gsarti
/

wmt_vat