Abstract
Sentence simplification aims to simplify the content and structure of complex sentences, and thus make them easier to interpret for human readers, and easier to process for downstream NLP applications. Recent advances in neural machine translation have paved the way for novel approaches to the task. In this paper, we adapt an architecture with augmented memory capacities called Neural Semantic Encoders (Munkhdalai and Yu, 2017) for sentence simplification. Our experiments demonstrate the effectiveness of our approach on different simplification datasets, both in terms of automatic evaluation measures and human judgments.- Anthology ID:
- N18-2013
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 79–85
- Language:
- URL:
- https://aclanthology.org/N18-2013
- DOI:
- 10.18653/v1/N18-2013
- Cite (ACL):
- Tu Vu, Baotian Hu, Tsendsuren Munkhdalai, and Hong Yu. 2018. Sentence Simplification with Memory-Augmented Neural Networks. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 79–85, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Sentence Simplification with Memory-Augmented Neural Networks (Vu et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/N18-2013.pdf
- Data
- Newsela, TurkCorpus, WikiLarge