A Structured Variational Autoencoder for Contextual Morphological Inflection
Lawrence Wolf-Sonkin, Jason Naradowsky, Sabrina J. Mielke, Ryan Cotterell
Abstract
Statistical morphological inflectors are typically trained on fully supervised, type-level data. One remaining open research question is the following: How can we effectively exploit raw, token-level data to improve their performance? To this end, we introduce a novel generative latent-variable model for the semi-supervised learning of inflection generation. To enable posterior inference over the latent variables, we derive an efficient variational inference procedure based on the wake-sleep algorithm. We experiment on 23 languages, using the Universal Dependencies corpora in a simulated low-resource setting, and find improvements of over 10% absolute accuracy in some cases.- Anthology ID:
- P18-1245
- Original:
- P18-1245v1
- Version 2:
- P18-1245v2
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2631–2641
- Language:
- URL:
- https://aclanthology.org/P18-1245
- DOI:
- 10.18653/v1/P18-1245
- Cite (ACL):
- Lawrence Wolf-Sonkin, Jason Naradowsky, Sabrina J. Mielke, and Ryan Cotterell. 2018. A Structured Variational Autoencoder for Contextual Morphological Inflection. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2631–2641, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- A Structured Variational Autoencoder for Contextual Morphological Inflection (Wolf-Sonkin et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/P18-1245.pdf
- Code
- additional community code