Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation

Hua Zheng, Damai Dai, Lei Li, Tianyu Liu, Zhifang Sui, Baobao Chang, Yang Liu


Abstract
In this paper, we tackle the task of Definition Generation (DG) in Chinese, which aims at automatically generating a definition for a word. Most existing methods take the source word as an indecomposable semantic unit. However, in parataxis languages like Chinese, word meanings can be composed using the word formation process, where a word (“桃花”, peach-blossom) is formed by formation components (“桃”, peach; “花”, flower) using a formation rule (Modifier-Head). Inspired by this process, we propose to enhance DG with word formation features. We build a formation-informed dataset, and propose a model DeFT, which Decomposes words into formation features, dynamically Fuses different features through a gating mechanism, and generaTes word definitions. Experimental results show that our method is both effective and robust.
Anthology ID:
2021.naacl-main.437
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5524–5531
Language:
URL:
https://aclanthology.org/2021.naacl-main.437
DOI:
10.18653/v1/2021.naacl-main.437
Bibkey:
Cite (ACL):
Hua Zheng, Damai Dai, Lei Li, Tianyu Liu, Zhifang Sui, Baobao Chang, and Yang Liu. 2021. Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5524–5531, Online. Association for Computational Linguistics.
Cite (Informal):
Decompose, Fuse and Generate: A Formation-Informed Method for Chinese Definition Generation (Zheng et al., NAACL 2021)
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