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        <title>ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge</title>
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        <namePart type="given">Vincent</namePart>
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    <name type="personal">
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            <title>Proceedings of the 18th BioNLP Workshop and Shared Task</title>
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            <namePart type="given">Dina</namePart>
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    <abstract>We report on our system for textual inference and question entailment in the medical domain for the ACL BioNLP 2019 Shared Task, MEDIQA. Textual inference is the task of finding the semantic relationships between pairs of text. Question entailment involves identifying pairs of questions which have similar semantic content. To improve upon medical natural language inference and question entailment approaches to further medical question answering, we propose a system that incorporates open-domain and biomedical domain approaches to improve semantic understanding and ambiguity resolution. Our models achieve 80% accuracy on medical natural language inference (6.5% absolute improvement over the original baseline), 48.9% accuracy on recognising medical question entailment, 0.248 Spearman’s rho for question answering ranking and 68.6% accuracy for question answering classification.</abstract>
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%0 Conference Proceedings
%T ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge
%A Nguyen, Vincent
%A Karimi, Sarvnaz
%A Xing, Zhenchang
%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 nguyen-etal-2019-anu
%X We report on our system for textual inference and question entailment in the medical domain for the ACL BioNLP 2019 Shared Task, MEDIQA. Textual inference is the task of finding the semantic relationships between pairs of text. Question entailment involves identifying pairs of questions which have similar semantic content. To improve upon medical natural language inference and question entailment approaches to further medical question answering, we propose a system that incorporates open-domain and biomedical domain approaches to improve semantic understanding and ambiguity resolution. Our models achieve 80% accuracy on medical natural language inference (6.5% absolute improvement over the original baseline), 48.9% accuracy on recognising medical question entailment, 0.248 Spearman’s rho for question answering ranking and 68.6% accuracy for question answering classification.
%R 10.18653/v1/W19-5051
%U https://aclanthology.org/W19-5051
%U https://doi.org/10.18653/v1/W19-5051
%P 478-487

Markdown (Informal)

[ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge](https://aclanthology.org/W19-5051) (Nguyen et al., BioNLP 2019)

• ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge (Nguyen et al., BioNLP 2019)

ACL

• Vincent Nguyen, Sarvnaz Karimi, and Zhenchang Xing. 2019. ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 478–487, Florence, Italy. Association for Computational Linguistics.