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Controllable text simplification with explicit paraphrasing

Maddela, Mounica, Alva-Manchego, Fernando and Xu, Wei 2021. Controllable text simplification with explicit paraphrasing. Presented at: 2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Virtual, 06-11 June 2021. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, pp. 3536-3553. 10.18653/v1/2021.naacl-main.277

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Text Simplification improves the readability of sentences through several rewriting transformations, such as lexical paraphrasing, deletion, and splitting. Current simplification systems are predominantly sequence-to-sequence models that are trained end-to-end to perform all these operations simultaneously. However, such systems limit themselves to mostly deleting words and cannot easily adapt to the requirements of different target audiences. In this paper, we propose a novel hybrid approach that leverages linguistically-motivated rules for splitting and deletion, and couples them with a neural paraphrasing model to produce varied rewriting styles. We introduce a new data augmentation method to improve the paraphrasing capability of our model. Through automatic and manual evaluations, we show that our proposed model establishes a new state-of-the-art for the task, paraphrasing more often than the existing systems, and can control the degree of each simplification operation applied to the input texts.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Additional Information: File licensed with a Creative Commons Attribution 4.0 International License
Publisher: Association for Computational Linguistics
Date of First Compliant Deposit: 14 February 2022
Last Modified: 14 Feb 2022 17:00

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