@inproceedings{zetsu-etal-2022-lexically,
title = "Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification",
author = "Zetsu, Tatsuya and
Kajiwara, Tomoyuki and
Arase, Yuki",
editor = "{\v{S}}tajner, Sanja and
Saggion, Horacio and
Ferr{\'e}s, Daniel and
Shardlow, Matthew and
Sheang, Kim Cheng and
North, Kai and
Zampieri, Marcos and
Xu, Wei",
booktitle = "Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Virtual)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.tsar-1.13",
doi = "10.18653/v1/2022.tsar-1.13",
pages = "147--153",
abstract = "Controllable text simplification assists language learners by automatically rewriting complex sentences into simpler forms of a target level. However, existing methods tend to perform conservative edits that keep complex words intact. To address this problem, we employ lexically constrained decoding to encourage rewriting. Specifically, the proposed method predicts edit operations conditioned to a target level and creates positive/negative constraints for words that should/should not appear in an output sentence. The experimental results confirm that our method significantly outperforms previous methods and demonstrates a new state-of-the-art performance.",
}
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<abstract>Controllable text simplification assists language learners by automatically rewriting complex sentences into simpler forms of a target level. However, existing methods tend to perform conservative edits that keep complex words intact. To address this problem, we employ lexically constrained decoding to encourage rewriting. Specifically, the proposed method predicts edit operations conditioned to a target level and creates positive/negative constraints for words that should/should not appear in an output sentence. The experimental results confirm that our method significantly outperforms previous methods and demonstrates a new state-of-the-art performance.</abstract>
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%0 Conference Proceedings
%T Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification
%A Zetsu, Tatsuya
%A Kajiwara, Tomoyuki
%A Arase, Yuki
%Y Štajner, Sanja
%Y Saggion, Horacio
%Y Ferrés, Daniel
%Y Shardlow, Matthew
%Y Sheang, Kim Cheng
%Y North, Kai
%Y Zampieri, Marcos
%Y Xu, Wei
%S Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Virtual)
%F zetsu-etal-2022-lexically
%X Controllable text simplification assists language learners by automatically rewriting complex sentences into simpler forms of a target level. However, existing methods tend to perform conservative edits that keep complex words intact. To address this problem, we employ lexically constrained decoding to encourage rewriting. Specifically, the proposed method predicts edit operations conditioned to a target level and creates positive/negative constraints for words that should/should not appear in an output sentence. The experimental results confirm that our method significantly outperforms previous methods and demonstrates a new state-of-the-art performance.
%R 10.18653/v1/2022.tsar-1.13
%U https://aclanthology.org/2022.tsar-1.13
%U https://doi.org/10.18653/v1/2022.tsar-1.13
%P 147-153
Markdown (Informal)
[Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification](https://aclanthology.org/2022.tsar-1.13) (Zetsu et al., TSAR 2022)
ACL