This paper presents our system for the BioASQ10b Phase B task. For ideal answers, we used the fine-tuned BioBERT model on the MNLI dataset to construct sentence embeddings and combined it with BERTScore to select sentences from the provided Snippets as answers. For the exact answers, we also used the BioBERT model and used the snippet scores generated from the ideal answer selection model to predict the exact answers for factoid and list questions. The exact answers of our fifth test batch ranked second place. In addition, the ideal answers we submitted achieved first place in the ROUGE score in all test batches from batch second to fifth.
|頁（從 - 到）
|CEUR Workshop Proceedings
|已出版 - 2022
|2022 Conference and Labs of the Evaluation Forum, CLEF 2022 - Bologna, Italy
持續時間: 5 9月 2022 → 8 9月 2022