An LLM-Enhanced Adversarial Editing System for Lexical Simplification
Keren Tan, Kangyang Luo, Yunshi Lan, Zheng Yuan, Jinlong Shu
Abstract
Lexical Simplification (LS) aims to simplify text at the lexical level. Existing methods rely heavily on annotated data, making it challenging to apply in low-resource scenarios. In this paper, we propose a novel LS method without parallel corpora. This method employs an Adversarial Editing System with guidance from a confusion loss and an invariance loss to predict lexical edits in the original sentences. Meanwhile, we introduce an innovative LLM-enhanced loss to enable the distillation of knowledge from Large Language Models (LLMs) into a small-size LS system. From that, complex words within sentences are masked and a Difficulty-aware Filling module is crafted to replace masked positions with simpler words. At last, extensive experimental results and analyses on three benchmark LS datasets demonstrate the effectiveness of our proposed method.- Anthology ID:
- 2024.lrec-main.102
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 1136–1146
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.102
- DOI:
- Cite (ACL):
- Keren Tan, Kangyang Luo, Yunshi Lan, Zheng Yuan, and Jinlong Shu. 2024. An LLM-Enhanced Adversarial Editing System for Lexical Simplification. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 1136–1146, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- An LLM-Enhanced Adversarial Editing System for Lexical Simplification (Tan et al., LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2024.lrec-main.102.pdf