@inproceedings{fca75f8e4e79407d9de5a45b3091c4e6,
title = "A BERT-based one-pass multi-task model for clinical temporal relation extraction",
abstract = "Recently BERT has achieved a state-of-theart 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.",
author = "Chen Lin and Timothy Miller and Dmitriy Dligach and Farig Sadeque and Steven Bethard and Guergana Savova",
note = "Publisher Copyright: {\textcopyright} Association for Computation Linguistics.; 19th SIGBioMed Workshop on Biomedical Language Processing, BioNLP 2020 at the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 ; Conference date: 09-07-2020",
year = "2020",
language = "English (US)",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "70--75",
booktitle = "BioNLP 2020 - 19th SIGBioMed Workshop on Biomedical Language Processing, Proceedings of the Workshop",
}