mlsanityData to 7 Oct 2026

MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoders

arXiv 2212.09855 ↗First posted 19 Dec 2022cs.CLcited by 5

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Abstract (arXiv)

In this paper we present our contribution to the TSAR-2022 Shared Task on Lexical Simplification of the EMNLP 2022 Workshop on Text Simplification, Accessibility, and Readability. Our approach builds on and extends the unsupervised lexical simplification system with pretrained encoders (LSBert) system in the following ways: For the subtask of simplification candidate selection, it utilizes a RoBERTa transformer language model and expands the size of the generated candidate list. For subsequent substitution ranking, it introduces a new feature weighting scheme and adopts a candidate filtering method based on textual entailment to maximize semantic similarity between the target word and its simplification. Our best-performing system improves LSBert by 5.9% accuracy and achieves second place out of 33 ranked solutions.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 5 of its 18 references that are in the map. First posted on arXiv 19 Dec 2022.

Builds on

5 of 18 references.

References · 5

  1. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  2. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,665
  3. 2022Lexical Simplification Benchmarks for English, Portuguese, and Spanishcited by 18
  4. 2022Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Featurescited by 10
  5. 2022Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patternscited by 8

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Citing papers per year: 2023: 5.

5 papers.