Edit-Constrained Decoding for Sentence Simplification
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929519788556288 |
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| author | Zetsu, Tatsuya Arase, Yuki Kajiwara, Tomoyuki |
| author_facet | Zetsu, Tatsuya Arase, Yuki Kajiwara, Tomoyuki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_19247 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Edit-Constrained Decoding for Sentence Simplification Zetsu, Tatsuya Arase, Yuki Kajiwara, Tomoyuki Computation and Language Artificial Intelligence 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. |
| title | Edit-Constrained Decoding for Sentence Simplification |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2409.19247 |