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Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning

✍️ Bill Yuchen Lin, Seyeon Lee, Xiaoyang Qiao, Xiang Ren
🏢 in Proc. of ACL 2021

Paper Github Download MickeyCorpus Download X-CSR Datasets


Intro

Abstract. Commonsense reasoning research has so far been limited to English. We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning (CSR) beyond English. We collect the Mickey Corpus, consisting of 561k sentences in 11 different languages, which can be used for analyzing and improving ML-LMs. We propose Mickey Probe, a language-agnostic probing task for fairly evaluating the common sense of popular ML-LMs across different languages. In addition, we also create two new datasets, X-CSQA and X-CODAH, by translating their English versions to 15 other languages, so that we can evaluate popular ML-LMs for cross-lingual commonsense reasoning. To improve the performance beyond English, we propose a simple yet effective method — multilingual contrastive pre-training (MCP). It significantly enhances sentence representations, yielding a large performance gain on both benchmarks.

Herein, we provide our resources and method for studying cross-lingual commonsense reasoning.

  • A multi-lingual corpus for MickeyProbe task towards analyzing and pre-training ML-LMs.
  • Two X-CSR datasets (i.e., X-CSQA and X-CODAH) for evaluation.
  • The multilingual contrastive pre-training (MCP) method for improving ML-LMs’ performance (on Github).

We also build X-CSR leaderboard so that people can compare their cross-lingual/multilingual models with each other in a unified evaluation protocol like X-GLUE and XTREME.

Citation

@inproceedings{lin-etal-2021-xcsr,
    title = "Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning",
    author = "Lin, Bill Yuchen and Lee, Seyeon and Qiao, Xiaoyang and Ren, Xiang",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics (ACL-IJCNLP 2021)",
    year = "2021",
    note={to appear}
}