Machine-Translated Knowledge Transfer for Commonsense Causal Reasoning

This paper studies the problem of multilingual causal reasoning in resource-poor languages. Existing approaches, translating into the most probable resource-rich language such as English, suffer in the presence of translation and language gaps between different cultural area, which leads to the loss of causality. To overcome these challenges, our goal is thus to identify key techniques to construct a new causality network of cause-effect terms, targeted for the machinetranslated English, but without any language-specific knowledge of resource-poor languages. In our evaluations with three languages, Korean, Chinese, and French, our proposed method consistently outperforms all baselines, achieving upto 69. 0% reasoning accuracy, which is close to the state-ofthe-art accuracy 70. 2% achieved on English.

Authors

• Jinyoung Yeo Yonsei University
• Geungyu Wang
• Hyunsouk Cho Department of Software and Computer Engineering, Ajou University Department of Artificial Intelligence, Ajou University
• Seungtaek Choi Yanolja
• Seung-won Hwang Seoul National University

Keywords

No keywords are indexed for this paper.