%0 Conference Proceedings %T Leveraging Word-Formation Knowledge for Chinese Word Sense Disambiguation %A Zheng, Hua %A Li, Lei %A Dai, Damai %A Chen, Deli %A Liu, Tianyu %A Sun, Xu %A Liu, Yang %Y Moens, Marie-Francine %Y Huang, Xuanjing %Y Specia, Lucia %Y Yih, Scott Wen-tau %S Findings of the Association for Computational Linguistics: EMNLP 2021 %D 2021 %8 November %I Association for Computational Linguistics %C Punta Cana, Dominican Republic %F zheng-etal-2021-leveraging-word %X In parataxis languages like Chinese, word meanings are constructed using specific word-formations, which can help to disambiguate word senses. However, such knowledge is rarely explored in previous word sense disambiguation (WSD) methods. In this paper, we propose to leverage word-formation knowledge to enhance Chinese WSD. We first construct a large-scale Chinese lexical sample WSD dataset with word-formations. Then, we propose a model FormBERT to explicitly incorporate word-formations into sense disambiguation. To further enhance generalizability, we design a word-formation predictor module in case word-formation annotations are unavailable. Experimental results show that our method brings substantial performance improvement over strong baselines. %R 10.18653/v1/2021.findings-emnlp.78 %U https://aclanthology.org/2021.findings-emnlp.78 %U https://doi.org/10.18653/v1/2021.findings-emnlp.78 %P 918-923