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

arXiv: https://arxiv.org/abs/2212.09855 · HTML: https://mlsanity.com/paper/2212.09855/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 19 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Lexical Simplification Benchmarks for English, Portuguese, and Spanish](https://mlsanity.com/paper/2209.05301/) (2022)
- [Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features](https://mlsanity.com/paper/2204.04629/) (2022)
- [Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns](https://mlsanity.com/paper/2203.08085/) (2022)

## Built on by (most cited first, 5 of 5)

- [Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplification](https://mlsanity.com/paper/2302.02888/) (2023)
- [Deep Learning Approaches to Lexical Simplification: A Survey](https://mlsanity.com/paper/2305.12000/) (2023)
- [Multilingual Controllable Transformer-Based Lexical Simplification](https://mlsanity.com/paper/2307.02120/) (2023)
- [Multilingual Lexical Simplification via Paraphrase Generation](https://mlsanity.com/paper/2307.15286/) (2023)
- [Unsupervised Lexical Simplification with Context Augmentation](https://mlsanity.com/paper/2311.00310/) (2023)

## 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.