Personalized Substitution Ranking for Lexical Simplification

Lee, John; Yeung, Chak Yan

doi:10.18653/v1/w19-8634

Cited by 18 publications

(20 citation statements)

References 17 publications

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“…Another drawback of these end-to-end neural systems is the lack of controllability. Simplification is highly audience dependant, and what constitutes simplified text for one group of users may not be acceptable for other groups (Xu et al, 2015;Lee and Yeung, 2018). An ideal simplification system should be able to generate text with varied characteristics, such as different lengths, readability levels, and number of split sentences, which can be difficult to control in end-to-end systems.…”

Section: Introductionmentioning

confidence: 99%

Controllable Text Simplification with Explicit Paraphrasing

Maddela¹,

Alva-Manchego²,

Xu³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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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-ofthe 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. 1

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Section: Introductionmentioning

confidence: 99%

Controllable Text Simplification with Explicit Paraphrasing

Maddela¹,

Alva-Manchego²,

Xu³

2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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“…It has been shown that accurate CWI can significantly reduce errors in simplification (Shardlow, 2014), thus improving the quality of an LS system output (Lee and Yeung, 2018). In addition, CWI has been shown to be an important component in readability assessment systems (Maddela and Xu, 2018) and in vocabulary acquisition modules of educational applications (Zaidi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Despite CWI being one of the key steps in an LS pipeline in need of adaptation to readers' profiles, this is rarely addressed in practice (Lee and Yeung, 2018;Bingel, 2018). For instance, existing and widely used datasets on CWI present a homogeneous view on word complexity, merging annotations from various groups of readers (Paetzold and Specia, 2016c;Yimam et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Word Complexity is in the Eye of the Beholder

Gooding

Kochmar²,

Yimam

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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Lexical complexity is a highly subjective notion, yet this factor is often neglected in lexical simplification and readability systems which use a "one-size-fits-all" approach. In this paper, we investigate which aspects contribute to the notion of lexical complexity in various groups of readers, focusing on native and nonnative speakers of English, and how the notion of complexity changes depending on the proficiency level of a non-native reader. To facilitate reproducibility of our approach and foster further research into these aspects, we release a dataset of complex words annotated by readers with different backgrounds.

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“…We believe this is a very complex task, which should be addressed as a whole and actually is (Yimam et al, 2018), especially because CWI requires to take into account the readers' characteristics. Methods based on lexical characteristics or word lists overlook the reader's characteristics and Lee and Yeung (2019) have rightly stressed that current approaches offer the same substitutions regardless of users. This tool is therefore based on the prerequisite that complex words already have been identified.…”

Section: Proposed Approachmentioning

confidence: 99%