@inproceedings{lin-etal-2020-bert,
title = "A {BERT}-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction",
author = "Lin, Chen and
Miller, Timothy and
Dligach, Dmitriy and
Sadeque, Farig and
Bethard, Steven and
Savova, Guergana",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.bionlp-1.7",
doi = "10.18653/v1/2020.bionlp-1.7",
pages = "70--75",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction
%A Lin, Chen
%A Miller, Timothy
%A Dligach, Dmitriy
%A Sadeque, Farig
%A Bethard, Steven
%A Savova, Guergana
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F lin-etal-2020-bert
%X 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.
%R 10.18653/v1/2020.bionlp-1.7
%U https://aclanthology.org/2020.bionlp-1.7
%U https://doi.org/10.18653/v1/2020.bionlp-1.7
%P 70-75
Markdown (Informal)
[A BERT-based One-Pass Multi-Task Model for Clinical Temporal Relation Extraction](https://aclanthology.org/2020.bionlp-1.7) (Lin et al., BioNLP 2020)
ACL