Abstract
We explore the task of multi-source morphological reinflection, which generalizes the standard, single-source version. The input consists of (i) a target tag and (ii) multiple pairs of source form and source tag for a lemma. The motivation is that it is beneficial to have access to more than one source form since different source forms can provide complementary information, e.g., different stems. We further present a novel extension to the encoder-decoder recurrent neural architecture, consisting of multiple encoders, to better solve the task. We show that our new architecture outperforms single-source reinflection models and publish our dataset for multi-source morphological reinflection to facilitate future research.- Anthology ID:
- E17-1049
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long 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:
- 514–524
- Language:
- URL:
- https://aclanthology.org/E17-1049
- DOI:
- Cite (ACL):
- Katharina Kann, Ryan Cotterell, and Hinrich Schütze. 2017. Neural Multi-Source Morphological Reinflection. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 514–524, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Neural Multi-Source Morphological Reinflection (Kann et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/E17-1049.pdf