Abstract
Controllable text simplification assists language learners by automatically rewriting complex sentences into simpler forms of a target level. However, existing methods tend to perform conservative edits that keep complex words intact. To address this problem, we employ lexically constrained decoding to encourage rewriting. Specifically, the proposed method predicts edit operations conditioned to a target level and creates positive/negative constraints for words that should/should not appear in an output sentence. The experimental results confirm that our method significantly outperforms previous methods and demonstrates a new state-of-the-art performance.- Anthology ID:
- 2022.tsar-1.13
- Volume:
- Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Virtual)
- Editors:
- Sanja Štajner, Horacio Saggion, Daniel Ferrés, Matthew Shardlow, Kim Cheng Sheang, Kai North, Marcos Zampieri, Wei Xu
- Venue:
- TSAR
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 147–153
- Language:
- URL:
- https://aclanthology.org/2022.tsar-1.13
- DOI:
- 10.18653/v1/2022.tsar-1.13
- Cite (ACL):
- Tatsuya Zetsu, Tomoyuki Kajiwara, and Yuki Arase. 2022. Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification. In Proceedings of the Workshop on Text Simplification, Accessibility, and Readability (TSAR-2022), pages 147–153, Abu Dhabi, United Arab Emirates (Virtual). Association for Computational Linguistics.
- Cite (Informal):
- Lexically Constrained Decoding with Edit Operation Prediction for Controllable Text Simplification (Zetsu et al., TSAR 2022)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2022.tsar-1.13.pdf