Recent successes in pre-trained language models, such as BERT, RoBERTa, and XLNet, have yielded state-of-the-art results in the natural language processing field. BioASQ is a question answering (QA) benchmark with a public and competitive leaderboard that spurs advancement in large-scale pre-trained language models for biomedical QA. In this paper, we introduce our system for the BioASQ Task 8b Phase B. We employed a pre-trained biomedical language model, BioBERT, to generate “exact” answers for the questions, and a logistic regression model with our sentence embedding to construct the top-n sentences/snippets as a prediction for “ideal” answers. On the final test batch, our best configuration achieved the highest ROUGE-2 and ROUGE-SU4 F1 scores among all participants in the 8th BioASQ QA task (Task 8b, Phase B).
|期刊||CEUR Workshop Proceedings|
|出版狀態||已出版 - 2020|
|事件||11th Conference and Labs of the Evaluation Forum, CLEF 2020 - Thessaloniki, Greece|
持續時間: 22 9月 2020 → 25 9月 2020