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.
BibTeX
@inproceedings{zheng-etal-2021-decompose,
title = "Decompose, Fuse and Generate: A Formation-Informed Method for {C}hinese Definition Generation",
author = "Zheng, Hua and
Dai, Damai and
Li, Lei and
Liu, Tianyu and
Sui, Zhifang and
Chang, Baobao and
Liu, Yang",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.437/",
doi = "10.18653/v1/2021.naacl-main.437",
pages = "5524--5531"
}