Abstract
Multi-task learning (MTL) has been studied recently for sequence labeling. Typically, auxiliary tasks are selected specifically in order to improve the performance of a target task. Jointly learning multiple tasks in a way that benefit all of them simultaneously can increase the utility of MTL. In order to do so, we propose a new LSTM cell which contains both shared parameters that can learn from all tasks, and task-specific parameters that can learn task-specific information. We name it a Shared-Cell Long-Short Term Memory SC-LSTM. Experimental results on three sequence labeling benchmarks (named-entity recognition, text chunking, and part-of-speech tagging) demonstrate the effectiveness of our SC-LSTM cell.- Anthology ID:
- N19-1249
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2396–2406
- Language:
- URL:
- https://aclanthology.org/N19-1249
- DOI:
- 10.18653/v1/N19-1249
- Cite (ACL):
- Peng Lu, Ting Bai, and Philippe Langlais. 2019. SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2396–2406, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- SC-LSTM: Learning Task-Specific Representations in Multi-Task Learning for Sequence Labeling (Lu et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/N19-1249.pdf
- Data
- CoNLL 2003, Universal Dependencies