A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction
Chen Lin, Timothy Miller, Dmitriy Dligach, Farig Sadeque, Steven Bethard, Guergana Savova
Abstract
Recently BERT has achieved a state-of-the-art performance in temporal relation extraction from clinical Electronic Medical Records text. However, the current approach is inefficient as it requires multiple passes through each input sequence. We extend a recently-proposed one-pass model for relation classification to a one-pass model for relation extraction. We augment this framework by introducing global embeddings to help with long-distance relation inference, and by multi-task learning to increase model performance and generalizability. Our proposed model produces results on par with the state-of-the-art in temporal relation extraction on the THYME corpus and is much “greener” in computational cost.- Anthology ID:
- 2020.bionlp-1.7
- Volume:
- Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dina Demner-Fushman, Kevin Bretonnel Cohen, Sophia Ananiadou, Junichi Tsujii
- Venue:
- BioNLP
- SIG:
- SIGBIOMED
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 70–75
- Language:
- URL:
- https://aclanthology.org/2020.bionlp-1.7
- DOI:
- 10.18653/v1/2020.bionlp-1.7
- Cite (ACL):
- Chen Lin, Timothy Miller, Dmitriy Dligach, Farig Sadeque, Steven Bethard, and Guergana Savova. 2020. A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction. In Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, pages 70–75, Online. Association for Computational Linguistics.
- Cite (Informal):
- A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction (Lin et al., BioNLP 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2020.bionlp-1.7.pdf