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Findings of the Association for Computational Linguistics: ACL 2022
Cross-lingual Entity Typing (CLET) aims at improving the quality of entity type prediction by transferring semantic knowledge learned from rich-resourced languages to low-resourced languages. In this paper, by utilizing multilingual transfer learning via the mixture-of-experts approach, our model dynamically capture the relationship between target language and each source language, and effectively generalize to predict types of unseen entities in new languages. Extensive experiments ondoi:10.18653/v1/2022.findings-acl.243 fatcat:mjgkbmkyovbxvh3ogvgt3ty5qe