In this paper, we detail our submission to the 2019, 7th year, BioASQ competition. We present our approach for Task-7b, Phase B, Exact Answering Task. These Question Answering (QA) tasks include Factoid, Yes/No, List Type Question answering. Our system is based on a contextual word embedding model. We have used a Bidirectional Encoder Representations from Transformers(BERT) based system, fined tuned for biomedical question answering task using BioBERT. In the third test batch set, our system achieved the highest MRR score for Factoid Question Answering task. Also, for List type question answering task our system achieved the highest recall score in the fourth test batch set. Along with our detailed approach, we present the results for our submissions, and also highlight identified downsides for our current approach and ways to improve them in our future experiments.
Cite
@article{arxiv.2002.01984,
title = {UNCC Biomedical Semantic Question Answering Systems. BioASQ: Task-7B, Phase-B},
author = {Sai Krishna Telukuntla and Aditya Kapri and Wlodek Zadrozny},
journal= {arXiv preprint arXiv:2002.01984},
year = {2020}
}
Comments
28 pages, 8 figures. This is an expanded version of our submission to 2019 BioAsq competition