Abstract
Sequence labeling architectures use word embeddings for capturing similarity, but suffer when handling previously unseen or rare words. We investigate character-level extensions to such models and propose a novel architecture for combining alternative word representations. By using an attention mechanism, the model is able to dynamically decide how much information to use from a word- or character-level component. We evaluated different architectures on a range of sequence labeling datasets, and character-level extensions were found to improve performance on every benchmark. In addition, the proposed attention-based architecture delivered the best results even with a smaller number of trainable parameters.- Anthology ID:
- C16-1030
- Volume:
- Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
- Month:
- December
- Year:
- 2016
- Address:
- Osaka, Japan
- Editors:
- Yuji Matsumoto, Rashmi Prasad
- Venue:
- COLING
- SIG:
- Publisher:
- The COLING 2016 Organizing Committee
- Note:
- Pages:
- 309–318
- Language:
- URL:
- https://aclanthology.org/C16-1030
- DOI:
- Cite (ACL):
- Marek Rei, Gamal Crichton, and Sampo Pyysalo. 2016. Attending to Characters in Neural Sequence Labeling Models. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 309–318, Osaka, Japan. The COLING 2016 Organizing Committee.
- Cite (Informal):
- Attending to Characters in Neural Sequence Labeling Models (Rei et al., COLING 2016)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/C16-1030.pdf
- Data
- FCE, Penn Treebank