Abstract
We present a new Lexical Simplification approach that exploits Neural Networks to learn substitutions from the Newsela corpus - a large set of professionally produced simplifications. We extract candidate substitutions by combining the Newsela corpus with a retrofitted context-aware word embeddings model and rank them using a new neural regression model that learns rankings from annotated data. This strategy leads to the highest Accuracy, Precision and F1 scores to date in standard datasets for the task.- Anthology ID:
- E17-2006
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 34–40
- Language:
- URL:
- https://aclanthology.org/E17-2006
- DOI:
- Cite (ACL):
- Gustavo Paetzold and Lucia Specia. 2017. Lexical Simplification with Neural Ranking. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 34–40, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Lexical Simplification with Neural Ranking (Paetzold & Specia, EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/E17-2006.pdf
- Data
- Newsela