XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization

Alessandro Raganato, Tommaso Pasini, Jose Camacho-Collados, Mohammad Taher Pilehvar


Abstract
The ability to correctly model distinct meanings of a word is crucial for the effectiveness of semantic representation techniques. However, most existing evaluation benchmarks for assessing this criterion are tied to sense inventories (usually WordNet), restricting their usage to a small subset of knowledge-based representation techniques. The Word-in-Context dataset (WiC) addresses the dependence on sense inventories by reformulating the standard disambiguation task as a binary classification problem; but, it is limited to the English language. We put forward a large multilingual benchmark, XL-WiC, featuring gold standards in 12 new languages from varied language families and with different degrees of resource availability, opening room for evaluation scenarios such as zero-shot cross-lingual transfer. We perform a series of experiments to determine the reliability of the datasets and to set performance baselines for several recent contextualized multilingual models. Experimental results show that even when no tagged instances are available for a target language, models trained solely on the English data can attain competitive performance in the task of distinguishing different meanings of a word, even for distant languages. XL-WiC is available at https://pilehvar.github.io/xlwic/.
Anthology ID:
2020.emnlp-main.584
Volume:
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Month:
November
Year:
2020
Address:
Online
Editors:
Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
7193–7206
Language:
URL:
https://aclanthology.org/2020.emnlp-main.584
DOI:
10.18653/v1/2020.emnlp-main.584
Bibkey:
Cite (ACL):
Alessandro Raganato, Tommaso Pasini, Jose Camacho-Collados, and Mohammad Taher Pilehvar. 2020. XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 7193–7206, Online. Association for Computational Linguistics.
Cite (Informal):
XL-WiC: A Multilingual Benchmark for Evaluating Semantic Contextualization (Raganato et al., EMNLP 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.emnlp-main.584.pdf
Video:
 https://slideslive.com/38938656
Data
XL-WiCWiC