A Dive into Lexical Simplification with Pre-trained Model-Reference-Cited by-同舟云学术

A Dive into Lexical Simplification with Pre-trained Model

Published:2023-10-27 Issue: Volume:54 Page:92-95
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Container-title:2023 4th International Conference on Machine Learning and Computer Application
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Author:

Yang Chengxue¹^ORCID,Wang Zhijuan²^ORCID,Zhang Yu²^ORCID,Zhao Xiaobing¹^ORCID

Affiliation:

1. National Language Resource Monitoring & Research Center Minority Languages Branch, Minzu University of China, China

2. Minzu University of China, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3650215.3650233

Reference17 articles.

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3. E. Pavlick and C. Callison-Burch. 2016. Simple PPDB: A Paraphrase Database for Simplification. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Berlin, Germany, 143-148

4. S. Narayan and C. Gardent. 2014. Hybrid Simplification using Deep Semantics and Machine Translation. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Baltimore, Maryland, 435-445

5. L. Cripwell J. Legrand and C. Gardent. 2022. Controllable Sentence Simplification via Operation Classification. In Findings of the Association for Computational Linguistics: NAACL. Association for Computational Linguistics Seattle United States 2091-2103