Abstract
We propose a sequence labeling framework with a secondary training objective, learning to predict surrounding words for every word in the dataset. This language modeling objective incentivises the system to learn general-purpose patterns of semantic and syntactic composition, which are also useful for improving accuracy on different sequence labeling tasks. The architecture was evaluated on a range of datasets, covering the tasks of error detection in learner texts, named entity recognition, chunking and POS-tagging. The novel language modeling objective provided consistent performance improvements on every benchmark, without requiring any additional annotated or unannotated data.- Anthology ID:
- P17-1194
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2121–2130
- Language:
- URL:
- https://aclanthology.org/P17-1194
- DOI:
- 10.18653/v1/P17-1194
- Cite (ACL):
- Marek Rei. 2017. Semi-supervised Multitask Learning for Sequence Labeling. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2121–2130, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Semi-supervised Multitask Learning for Sequence Labeling (Rei, ACL 2017)
- PDF:
- https://preview.aclanthology.org/add_acl24_videos/P17-1194.pdf
- Code
- marekrei/sequence-labeler + additional community code
- Data
- CoNLL, CoNLL 2003, FCE, Penn Treebank