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        <title>ChiMed: A Chinese Medical Corpus for Question Answering</title>
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        <namePart type="given">Yuanhe</namePart>
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        <namePart type="given">Weicheng</namePart>
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            <title>Proceedings of the 18th BioNLP Workshop and Shared Task</title>
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    <abstract>Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains. While models trained in the general domain can be adapted to a new target domain, their performance often degrades significantly due to domain mismatch. Alternatively, one can require a large amount of domain-specific QA data, but such data are rare, especially for the medical domain. In this study, we first collect a large-scale Chinese medical QA corpus called ChiMed; second we annotate a small fraction of the corpus to check the quality of the answers; third, we extract two datasets from the corpus and use them for the relevancy prediction task and the adoption prediction task. Several benchmark models are applied to the datasets, producing good results for both tasks.</abstract>
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%0 Conference Proceedings
%T ChiMed: A Chinese Medical Corpus for Question Answering
%A Tian, Yuanhe
%A Ma, Weicheng
%A Xia, Fei
%A Song, Yan
%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 tian-etal-2019-chimed
%X Question answering (QA) is a challenging task in natural language processing (NLP), especially when it is applied to specific domains. While models trained in the general domain can be adapted to a new target domain, their performance often degrades significantly due to domain mismatch. Alternatively, one can require a large amount of domain-specific QA data, but such data are rare, especially for the medical domain. In this study, we first collect a large-scale Chinese medical QA corpus called ChiMed; second we annotate a small fraction of the corpus to check the quality of the answers; third, we extract two datasets from the corpus and use them for the relevancy prediction task and the adoption prediction task. Several benchmark models are applied to the datasets, producing good results for both tasks.
%R 10.18653/v1/W19-5027
%U https://aclanthology.org/W19-5027
%U https://doi.org/10.18653/v1/W19-5027
%P 250-260

Markdown (Informal)

[ChiMed: A Chinese Medical Corpus for Question Answering](https://aclanthology.org/W19-5027) (Tian et al., BioNLP 2019)

• ChiMed: A Chinese Medical Corpus for Question Answering (Tian et al., BioNLP 2019)

ACL

• Yuanhe Tian, Weicheng Ma, Fei Xia, and Yan Song. 2019. ChiMed: A Chinese Medical Corpus for Question Answering. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 250–260, Florence, Italy. Association for Computational Linguistics.