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    <abstract>Text simplification is a task to reduce the complexity of a text while retain its original meaning. It can facilitate people with low-literacy skills or language impairments, such as children and individuals with dyslexia and aphasia, to read and understand complicated materials. Normally, substitution, deletion, reordering, and splitting are considered as four core operations for performing text simplification. Thus an ideal model should be capable of executing these operations appropriately to simplify a text. However, by examining the degree that each operation is exerted in different datasets, we observe that there is a salient discrepancy between the human annotation and existing training data that is widely used for training simplification models. To alleviate this discrepancy, we propose an unsupervised data construction method that distills each simplifying operation into data via different automatic data enhancement measures. The empirical results demonstrate that the resulting dataset SimSim can support models to achieve better performance by performing all operations properly.</abstract>
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%0 Conference Proceedings
%T Divide-and-Conquer Text Simplification by Scalable Data Enhancement
%A Zhao, Sanqiang
%A Meng, Rui
%A Su, Hui
%A He, Daqing
%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 zhao-etal-2022-divide
%X Text simplification is a task to reduce the complexity of a text while retain its original meaning. It can facilitate people with low-literacy skills or language impairments, such as children and individuals with dyslexia and aphasia, to read and understand complicated materials. Normally, substitution, deletion, reordering, and splitting are considered as four core operations for performing text simplification. Thus an ideal model should be capable of executing these operations appropriately to simplify a text. However, by examining the degree that each operation is exerted in different datasets, we observe that there is a salient discrepancy between the human annotation and existing training data that is widely used for training simplification models. To alleviate this discrepancy, we propose an unsupervised data construction method that distills each simplifying operation into data via different automatic data enhancement measures. The empirical results demonstrate that the resulting dataset SimSim can support models to achieve better performance by performing all operations properly.
%R 10.18653/v1/2022.tsar-1.15
%U https://aclanthology.org/2022.tsar-1.15
%U https://doi.org/10.18653/v1/2022.tsar-1.15
%P 166-172

Markdown (Informal)

[Divide-and-Conquer Text Simplification by Scalable Data Enhancement](https://aclanthology.org/2022.tsar-1.15) (Zhao et al., TSAR 2022)

• Divide-and-Conquer Text Simplification by Scalable Data Enhancement (Zhao et al., TSAR 2022)

ACL

• Sanqiang Zhao, Rui Meng, Hui Su, and Daqing He. 2022. Divide-and-Conquer Text Simplification by Scalable Data Enhancement. In Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), pages 166–172, Abu Dhabi, United Arab Emirates (Virtual). Association for Computational Linguistics.