Automatic Question Answering for Medical MCQs: Can It go Further than Information Retrieval?

Le An Ha, Victoria Yaneva


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.
Anthology ID:
R19-1049
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
418–422
Language:
URL:
https://www.aclweb.org/anthology/R19-1049
DOI:
10.26615/978-954-452-056-4_049
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PDF:
http://aclanthology.lst.uni-saarland.de/R19-1049.pdf