Pith. sign in

REVIEW 1 cited by

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

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2212.09855 v1 pith:FUGBOH6F submitted 2022-12-19 cs.CL

Xiaofei Li , Daniel Wiechmann , Yu Qiao , Elma Kerz This is my paper · ORCID
classification cs.CL
keywords simplificationcandidatelexicalsystemencoderslsbertpretrainedshared
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Redefining Simplicity: Benchmarking Large Language Models from Lexical to Document Simplification

    cs.CL 2025-02 conditional novelty 4.0 of 10

    In a four-task benchmark, GPT-4o, Llama3.1-70B, and Gemma2-2B outperform traditional text simplification systems on most automatic metrics, and GPT-4o is preferred over human-written references in a small human study.

Pith tools