Neural Question Answering at BioASQ 5B

Georg Wiese, Dirk Weissenborn, Mariana Neves


Abstract
This paper describes our submission to the 2017 BioASQ challenge. We participated in Task B, Phase B which is concerned with biomedical question answering (QA). We focus on factoid and list question, using an extractive QA model, that is, we restrict our system to output substrings of the provided text snippets. At the core of our system, we use FastQA, a state-of-the-art neural QA system. We extended it with biomedical word embeddings and changed its answer layer to be able to answer list questions in addition to factoid questions. We pre-trained the model on a large-scale open-domain QA dataset, SQuAD, and then fine-tuned the parameters on the BioASQ training set. With our approach, we achieve state-of-the-art results on factoid questions and competitive results on list questions.
Anthology ID:
W17-2309
Volume:
BioNLP 2017
Month:
August
Year:
2017
Address:
Vancouver, Canada,
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
76–79
Language:
URL:
https://www.aclweb.org/anthology/W17-2309
DOI:
10.18653/v1/W17-2309
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PDF:
http://aclanthology.lst.uni-saarland.de/W17-2309.pdf
Presentation:
 W17-2309.Presentation.pdf