Name
Abstract | ||
|---|---|---|
The recent success of question answering systems is largely attributed to pre-trained language models. However, as language models are mostly pre-trained on general domain corpora such as Wikipedia, they often have difficulty in understanding biomedical questions. In this paper, we investigate the performance of BioBERT, a pre-trained biomedical language model, in answering biomedical questions including factoid, list, and yes/no type questions. BioBERT uses almost the same structure across various question types and achieved the best performance in the 7th BioASQ Challenge (Task 7b, Phase B). BioBERT pre-trained on SQuAD or SQuAD 2.0 easily outperformed previous state-of-the-art models. BioBERT obtains the best performance when it uses the appropriate pre-/post-processing strategies for questions, passages, and answers. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-43887-6_64 | PKDD/ECML Workshops |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wonjin Yoon | 1 | 4 | 2.11 |
| Jinhyuk Lee | 2 | 0 | 1.01 |
| Donghyeon Kim | 3 | 100 | 7.37 |
| Minbyul Jeong | 4 | 4 | 2.11 |
| Jaewoo Kang | 5 | 1258 | 179.45 |