← 返回论文检索
EMNLP 2024emnlpfindings

Edit-Constrained Decoding for Sentence Simplification

Tatsuya Zetsu, Yuki Arase, Tomoyuki Kajiwara

LY Corporation · Tokyo Institute of Technology, Tokyo Institute of Technology and AIST, National Institute of Advanced Industrial Science and Technology · Ehime University

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.18653/v1/2024.findings-emnlp.419 ↗

摘要

We propose edit operation based lexically constrained decoding for sentence simplification. In sentence simplification, lexical paraphrasing is one of the primary procedures for rewriting complex sentences into simpler correspondences. While previous studies have confirmed the efficacy of lexically constrained decoding on this task, their constraints can be loose and may lead to sub-optimal generation. We address this problem by designing constraints that replicate the edit operations conducted in simplification and defining stricter satisfaction conditions. Our experiments indicate that the proposed method consistently outperforms the previous studies on three English simplification corpora commonly used in this task.