GLUECoS: An Evaluation Benchmark for Code-Switched NLP

Simran Khanuja, Sandipan Dandapat, Anirudh Srinivasan, Sunayana Sitaram, Monojit Choudhury


Abstract
Code-switching is the use of more than one language in the same conversation or utterance. Recently, multilingual contextual embedding models, trained on multiple monolingual corpora, have shown promising results on cross-lingual and multilingual tasks. We present an evaluation benchmark, GLUECoS, for code-switched languages, that spans several NLP tasks in English-Hindi and English-Spanish. Specifically, our evaluation benchmark includes Language Identification from text, POS tagging, Named Entity Recognition, Sentiment Analysis, Question Answering and a new task for code-switching, Natural Language Inference. We present results on all these tasks using cross-lingual word embedding models and multilingual models. In addition, we fine-tune multilingual models on artificially generated code-switched data. Although multilingual models perform significantly better than cross-lingual models, our results show that in most tasks, across both language pairs, multilingual models fine-tuned on code-switched data perform best, showing that multilingual models can be further optimized for code-switching tasks.
Anthology ID:
2020.acl-main.329
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3575–3585
Language:
URL:
https://www.aclweb.org/anthology/2020.acl-main.329
DOI:
10.18653/v1/2020.acl-main.329
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PDF:
http://aclanthology.lst.uni-saarland.de/2020.acl-main.329.pdf
Video:
 http://slideslive.com/38928983