A hybrid approach to DBQA

Abstract

Document-based question answering (DBQA) is a sub-task of open-domain question answering, targeted at selecting the answer sentence(s) from the given documents for a question. In this paper, we propose a hybrid approach to select answer sentences, combining existing models via the rank SVM model. Specifically, we capture the inter-relationship between the question and answer sentences from three aspects: surface string similarity, deep semantic similarity and relevance based on information retrieval models. Our experiments show that an improved retrieval model out-performs other methods, including the deep learning models. And, applying a rank SVM model to combine all these features, we achieve 0.8120 in mean reciprocal rank (MRR) and 0.8111 in mean average precision (MAP) in the opening test.