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

13 Jun 2021ACL 2021 5arXiv:2106.06937archive 2025-07-28

Bill Yuchen Lin, Seyeon Lee, Xiaoyang Qiao, Xiang Ren

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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convert_ABC_to_123 INK-USC/XCSR/xcsr_experiments/utils_multiple_choice.py official repository unverified MIT (permissive) · 01dbc46b29880c2d · report
convert_examples_to_features INK-USC/XCSR/xcsr_experiments/utils_multiple_choice.py official repository unverified MIT (permissive) · c04ec1353ed9d926 · report
eval_npy INK-USC/XCSR/xcsr_experiments/eval_utils.py official repository unverified MIT (permissive) · 77aa6eb953e2376d · report
output_labels INK-USC/XCSR/xcsr_experiments/eval_utils.py official repository unverified MIT (permissive) · 95bb1d5c24ca301e · report
simple_accuracy INK-USC/XCSR/xcsr_experiments/run_mcqa.py official repository unverified MIT (permissive) · 3c241ecfe3749a6d · report

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Common Sense ReasoningSentence

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