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Unsupervised Lexical Simplification with Context Augmentation

arXiv 2311.00310 ↗First posted 1 Nov 2023cs.CL cs.AIcited by 0

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Abstract (arXiv)

We propose a new unsupervised lexical simplification method that uses only monolingual data and pre-trained language models. Given a target word and its context, our method generates substitutes based on the target context and also additional contexts sampled from monolingual data. We conduct experiments in English, Portuguese, and Spanish on the TSAR-2022 shared task, and show that our model substantially outperforms other unsupervised systems across all languages. We also establish a new state-of-the-art by ensembling our model with GPT-3.5. Lastly, we evaluate our model on the SWORDS lexical substitution data set, achieving a state-of-the-art result.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 24 references that are in the map. First posted on arXiv 1 Nov 2023.

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References · 16

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2022Training language models to follow instructions with human feedbackcited by 15,971
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  5. 2020mT5: A massively multilingual pre-trained text-to-text transformercited by 1,818
  6. 2016Enriching Word Vectors with Subword Informationcited by 1,583
  7. 2021DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharingcited by 1,254
  8. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  9. 2023Spanish Pre-trained BERT Model and Evaluation Datacited by 81
  10. 2023Findings of the TSAR-2022 Shared Task on Multilingual Lexical Simplificationcited by 20
  11. 2022Lexical Simplification Benchmarks for English, Portuguese, and Spanishcited by 18
  12. 2021Swords: A Benchmark for Lexical Substitution with Improved Data Coverage and Qualitycited by 13
4 more
  1. 2023UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?cited by 12
  2. 2020LSBert: A Simple Framework for Lexical Simplificationcited by 9
  3. 2022MANTIS at TSAR-2022 Shared Task: Improved Unsupervised Lexical Simplification with Pretrained Encoderscited by 5
  4. 2022ALEXSIS-PT: A New Resource for Portuguese Lexical Simplificationcited by 3

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