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 language | English |
|---|---|
| Pages (from-to) | 351-359 |
| Number of pages | 9 |
| Journal | CEUR Workshop Proceedings |
| Volume | 2936 |
| Publication status | Published - 2021 |
| Event | 2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021 - Virtual, Bucharest, Romania Duration: 2021 Sept 21 → 2021 Sept 24 |
Bibliographical note
Funding Information:We express gratitude towards Dr. Jihye Kim and Dr. Sungjoon Park from Korea University for their invaluable insight into our systems’ output. This research is supported by National Research Foundation of Korea (NRF-2020R1A2C3010638) and a grant of the the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR20C0021)
Funding Information:
We express gratitude towards Dr. Jihye Kim and Dr. Sungjoon Park from Korea University for their invaluable insight into our systems' output. This research is supported by National Research Foundation of Korea (NRF-2020R1A2C3010638) and a grant of the the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HR20C0021)
Publisher Copyright:
© 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Keywords
- BioASQ
- Biomedical natural language processing
- Biomedical question answering
- BioNLP
ASJC Scopus subject areas
- General Computer Science