@inproceedings{bannihatti-kumar-etal-2019-dr,
title = "{D}r.{Q}uad at {MEDIQA} 2019: Towards Textual Inference and Question Entailment using contextualized representations",
author = "Bannihatti Kumar, Vinayshekhar and
Srinivasan, Ashwin and
Chaudhary, Aditi and
Route, James and
Mitamura, Teruko and
Nyberg, Eric",
editor = "Demner-Fushman, Dina and
Cohen, Kevin Bretonnel and
Ananiadou, Sophia and
Tsujii, Junichi",
booktitle = "Proceedings of the 18th BioNLP Workshop and Shared Task",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5048",
doi = "10.18653/v1/W19-5048",
pages = "453--461",
abstract = "This paper presents the submissions by TeamDr.Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain. Our system is based on the prior work Liu et al. (2019) which uses a multi-task objective function for textual entailment. In this work, we explore different strategies for generalizing state-of-the-art language understanding models to the specialized medical domain. Our results on the shared task demonstrate that incorporating domain knowledge through data augmentation is a powerful strategy for addressing challenges posed specialized domains such as medicine.",
}
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<abstract>This paper presents the submissions by TeamDr.Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain. Our system is based on the prior work Liu et al. (2019) which uses a multi-task objective function for textual entailment. In this work, we explore different strategies for generalizing state-of-the-art language understanding models to the specialized medical domain. Our results on the shared task demonstrate that incorporating domain knowledge through data augmentation is a powerful strategy for addressing challenges posed specialized domains such as medicine.</abstract>
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%0 Conference Proceedings
%T Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations
%A Bannihatti Kumar, Vinayshekhar
%A Srinivasan, Ashwin
%A Chaudhary, Aditi
%A Route, James
%A Mitamura, Teruko
%A Nyberg, Eric
%Y Demner-Fushman, Dina
%Y Cohen, Kevin Bretonnel
%Y Ananiadou, Sophia
%Y Tsujii, Junichi
%S Proceedings of the 18th BioNLP Workshop and Shared Task
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F bannihatti-kumar-etal-2019-dr
%X This paper presents the submissions by TeamDr.Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain. Our system is based on the prior work Liu et al. (2019) which uses a multi-task objective function for textual entailment. In this work, we explore different strategies for generalizing state-of-the-art language understanding models to the specialized medical domain. Our results on the shared task demonstrate that incorporating domain knowledge through data augmentation is a powerful strategy for addressing challenges posed specialized domains such as medicine.
%R 10.18653/v1/W19-5048
%U https://aclanthology.org/W19-5048
%U https://doi.org/10.18653/v1/W19-5048
%P 453-461
Markdown (Informal)
[Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations](https://aclanthology.org/W19-5048) (Bannihatti Kumar et al., BioNLP 2019)
ACL