BioBERT for Multiple Knowledge-Based Question Expansion and Biomedical Extractive Question Answering

Seeking a relevant answer to a biomedical question became a daily activity not only for experts but also for patients. In this perspective, biomedical extractive Question Answering systems have witnessed a rapid progress especially with the emergence of pre-trained language models such as BERT and its biomedical variant BioBERT. Those systems aim to extract an answer to a given question from a biomedical context and rely on two principal components question processing and exact answer identification. Several pre-trained language models-based systems have been proposed and focused only on the second component. In this paper, we proposed a BioBERT-based question answering system which rests on a question expansion phase. The Latter intends to extract question terms synonyms, as expansion terms, from multiple knowledge resource MeSH and WordNet. Indeed, we used firstly BioBERT pre-training model as a representation model in the selection of relevant expansion MeSH and WordNet terms. Secondly, in the fine-tuning phase to perform the question answering task and identify the exact answer. The experimental results on BioASQ dataset highlight the interest of the BioBert-based question expansion phase.