KU-DMIS at BioASQ 9: Data-centric and model-centric approaches for biomedical question answering

Wonjin Yoon, Jaehyo Yoo, Sumin Seo, Mujeen Sung, Minbyul Jeong, Gangwoo Kim, Jaewoo Kang

Research output: Contribution to journalConference articlepeer-review

Abstract

In this paper, we present approaches for our participation in the 9th BioASQ challenge (Task b - Phase B). Our systems are based on the transformer models with model-centric and data-centric approaches. For factoid-type questions we modified the dataset to increase label consistency, and for list-type questions we apply the sequence tagging model which is a more natural model design for the multi-label task. Our experimental results suggest two main points: better model design can be achieved by reflecting data characteristics such as the number of labels for a data point; and scarce resources such as BioQA datasets can greatly benefit from a data-centric approach with relatively little effort. Our submissions achieve competitive results with top or near top performance in the challenge.

Original languageEnglish
Pages (from-to)351-359
Number of pages9
JournalCEUR Workshop Proceedings
Volume2936
Publication statusPublished - 2021
Event2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021 - Virtual, Bucharest, Romania
Duration: 2021 Sep 212021 Sep 24

Keywords

  • BioASQ
  • Biomedical natural language processing
  • Biomedical question answering
  • BioNLP

ASJC Scopus subject areas

  • Computer Science(all)

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