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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 models accuracy compared to standard fine-tuning.
The question answering system can answer questions from various fields and forms with deep neural networks, but it still lacks effective ways when facing multiple evidences. We introduce a new model called SRQA, which means Synthetic Reader for Facto
We introduce DELFT, a factoid question answering system which combines the nuance and depth of knowledge graph question answering approaches with the broader coverage of free-text. DELFT builds a free-text knowledge graph from Wikipedia, with entitie
We present some categorical investigations into Wittgensteins language-games, with applications to game-theoretic pragmatics and question-answering in natural language processing.
To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diver
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 biomedi