@inproceedings{li-etal-2020-explicit,
title = "Explicit Semantic Decomposition for Definition Generation",
author = "Li, Jiahuan and
Bao, Yu and
Huang, Shujian and
Dai, Xinyu and
Chen, Jiajun",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.65/",
doi = "10.18653/v1/2020.acl-main.65",
pages = "708--717",
abstract = "Definition generation, which aims to automatically generate dictionary definitions for words, has recently been proposed to assist the construction of dictionaries and help people understand unfamiliar texts. However, previous works hardly consider explicitly modeling the {\textquotedblleft}components{\textquotedblright} of definitions, leading to under-specific generation results. In this paper, we propose ESD, namely Explicit Semantic Decomposition for definition Generation, which explicitly decomposes the meaning of words into semantic components, and models them with discrete latent variables for definition generation. Experimental results show that achieves top results on WordNet and Oxford benchmarks, outperforming strong previous baselines."
}
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<abstract>Definition generation, which aims to automatically generate dictionary definitions for words, has recently been proposed to assist the construction of dictionaries and help people understand unfamiliar texts. However, previous works hardly consider explicitly modeling the “components” of definitions, leading to under-specific generation results. In this paper, we propose ESD, namely Explicit Semantic Decomposition for definition Generation, which explicitly decomposes the meaning of words into semantic components, and models them with discrete latent variables for definition generation. Experimental results show that achieves top results on WordNet and Oxford benchmarks, outperforming strong previous baselines.</abstract>
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%0 Conference Proceedings
%T Explicit Semantic Decomposition for Definition Generation
%A Li, Jiahuan
%A Bao, Yu
%A Huang, Shujian
%A Dai, Xinyu
%A Chen, Jiajun
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F li-etal-2020-explicit
%X Definition generation, which aims to automatically generate dictionary definitions for words, has recently been proposed to assist the construction of dictionaries and help people understand unfamiliar texts. However, previous works hardly consider explicitly modeling the “components” of definitions, leading to under-specific generation results. In this paper, we propose ESD, namely Explicit Semantic Decomposition for definition Generation, which explicitly decomposes the meaning of words into semantic components, and models them with discrete latent variables for definition generation. Experimental results show that achieves top results on WordNet and Oxford benchmarks, outperforming strong previous baselines.
%R 10.18653/v1/2020.acl-main.65
%U https://aclanthology.org/2020.acl-main.65/
%U https://doi.org/10.18653/v1/2020.acl-main.65
%P 708-717
Markdown (Informal)
[Explicit Semantic Decomposition for Definition Generation](https://aclanthology.org/2020.acl-main.65/) (Li et al., ACL 2020)
ACL
- Jiahuan Li, Yu Bao, Shujian Huang, Xinyu Dai, and Jiajun Chen. 2020. Explicit Semantic Decomposition for Definition Generation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 708–717, Online. Association for Computational Linguistics.