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        <title>Automatic Question Answering for Medical MCQs: Can It go Further than Information Retrieval?</title>
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    <abstract>We present a novel approach to automatic question answering that does not depend on the performance of an information retrieval (IR) system and does not require that the training data come from the same source as the questions. We evaluate the system performance on a challenging set of university-level medical science multiple-choice questions. Best performance is achieved when combining a neural approach with an IR approach, both of which work independently. Unlike previous approaches, the system achieves statistically significant improvement over the random guess baseline even for questions that are labeled as challenging based on the performance of baseline solvers.</abstract>
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%0 Conference Proceedings
%T Automatic Question Answering for Medical MCQs: Can It go Further than Information Retrieval?
%A Ha, Le An
%A Yaneva, Victoria
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
%D 2019
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F ha-yaneva-2019-automatic
%X We present a novel approach to automatic question answering that does not depend on the performance of an information retrieval (IR) system and does not require that the training data come from the same source as the questions. We evaluate the system performance on a challenging set of university-level medical science multiple-choice questions. Best performance is achieved when combining a neural approach with an IR approach, both of which work independently. Unlike previous approaches, the system achieves statistically significant improvement over the random guess baseline even for questions that are labeled as challenging based on the performance of baseline solvers.
%R 10.26615/978-954-452-056-4_049
%U https://aclanthology.org/R19-1049
%U https://doi.org/10.26615/978-954-452-056-4_049
%P 418-422

Markdown (Informal)

[Automatic Question Answering for Medical MCQs: Can It go Further than Information Retrieval?](https://aclanthology.org/R19-1049) (Ha & Yaneva, RANLP 2019)

• Automatic Question Answering for Medical MCQs: Can It go Further than Information Retrieval? (Ha & Yaneva, RANLP 2019)

ACL

• Le An Ha and Victoria Yaneva. 2019. Automatic Question Answering for Medical MCQs: Can It go Further than Information Retrieval?. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 418–422, Varna, Bulgaria. INCOMA Ltd..