Semantically Corroborating Neural Attention for Biomedical Question Answering

Abstract

Biomedical question answering is a great challenge in NLP due to complex scientific vocabulary and lack of massive annotated corpora, but, at the same time, is full of potential in optimizing in critical ways the biomedical practices. This paper describes the work carried out as a part of the BioASQ challenge (Task-7B Phase-B), and targets an integral step in the question answering process: extractive answer selection. This deals with the identification of the exact answer (words, phrases or sentences) from given article snippets that are related to the question at hand. We address this problem in the context of factoid and summarization question types, using a variety of deep learning and semantic methods, including various architectures (e.g., Dynamic Memory Networks and Bidirectional Attention Flow), transfer learning, biomedical named entity recognition and corroboration of semantic evidence. On the top of candidate answer selection module, answer prediction to yes/no question types is also addressed by incorporating a sentiment analysis approach. The evaluation with respect to Rouge, MRR and F1 scores, in relation to the type of question answering task being considered, exhibits the potential of this hybrid method in extracting the correct answer to a question. In addition, the proposed corroborating semantics module can be added on top of the typical QA pipeline to gain a measured 5% improvement in identifying the exact answer with respect to the gold standard.