Transformer-based Language Models for Factoid Question Answering at BioASQ9b

• URL: http://arxiv.org/abs/2109.07185v1
• Date: Wed, 15 Sep 2021 10:01:06 GMT
• Title: Transformer-based Language Models for Factoid Question Answering at BioASQ9b
• Authors: Urvashi Khanna and Diego Moll\'a
• Abstract summary: We describe our experiments and participating systems in the BioASQ Task 9b Phase B challenge of biomedical question answering. For factoid questions, our ALBERT-based systems ranked first in test batch 1 and fourth in test batch 2. Our DistilBERT systems outperformed the ALBERT variants in test batches 4 and 5 despite having 81% fewer parameters than ALBERT.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work, we describe our experiments and participating systems in the BioASQ Task 9b Phase B challenge of biomedical question answering. We have focused on finding the ideal answers and investigated multi-task fine-tuning and gradual unfreezing techniques on transformer-based language models. For factoid questions, our ALBERT-based systems ranked first in test batch 1 and fourth in test batch 2. Our DistilBERT systems outperformed the ALBERT variants in test batches 4 and 5 despite having 81% fewer parameters than ALBERT. However, we observed that gradual unfreezing had no significant impact on the model's accuracy compared to standard fine-tuning.

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