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

• URL: http://arxiv.org/abs/2106.06937v1
• Date: Sun, 13 Jun 2021 07:14:03 GMT
• Title: Common Sense Beyond English: Evaluating and Improving Multilingual Language Models for Commonsense Reasoning
• Authors: Bill Yuchen Lin, Seyeon Lee, Xiaoyang Qiao, Xiang Ren
• Abstract summary: We aim to evaluate and improve popular multilingual language models (ML-LMs) to help advance commonsense reasoning 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.
• Score: 33.34063636400519
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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