Context-Aware Prediction of Derivational Word-forms
Ekaterina Vylomova, Ryan Cotterell, Timothy Baldwin, Trevor Cohn
Abstract
Derivational morphology is a fundamental and complex characteristic of language. In this paper we propose a new task of predicting the derivational form of a given base-form lemma that is appropriate for a given context. We present an encoder-decoder style neural network to produce a derived form character-by-character, based on its corresponding character-level representation of the base form and the context. We demonstrate that our model is able to generate valid context-sensitive derivations from known base forms, but is less accurate under lexicon agnostic setting.- Anthology ID:
- E17-2019
- 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:
- 118–124
- Language:
- URL:
- https://aclanthology.org/E17-2019
- DOI:
- Cite (ACL):
- Ekaterina Vylomova, Ryan Cotterell, Timothy Baldwin, and Trevor Cohn. 2017. Context-Aware Prediction of Derivational Word-forms. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 118–124, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Context-Aware Prediction of Derivational Word-forms (Vylomova et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/E17-2019.pdf
- Code
- ivri/dmorph