QHAN: Quantum-inspired Hierarchical Attention Mechanism Network for Question Answering

Abstract

The approach to question answering is challenging because it usually requires finding useful information from within and between question and answer sentences for sentence semantic matching. The key information mined from existing question and answer sentences, as a supplement to semantic information, maybe helpful for this task. However, capturing the intra-sentence and inter-sentence semantic interactions is somewhat difficult given the implicit interrelationships between question and answer sentences. Although the learning effect of multi-layer neural network is good, it lacks explanatory and theoretical support. This paper mainly studies how to select the best answer sentence from a set of question and answer sentences, mine information more effectively, perform semantic matching, and have a certain interpretability. Therefore, we propose a hierarchical attention network (QHAN) based on the mathematical framework of quantum theory, which integrates the attention mechanism under quantum measurement and density matrix (DMATT) and the attention mechanism under quantum weak measurement and weak value (WMATT) in one in a unified model framework. QHAN can not only discover key sentences in a set of question and answer sentences and key words in a sentence, but also perform sentence semantic matching quickly and accurately. Furthermore, QHAN has the advantage of interpretability in terms of models due to the physical meaning and attentional full weight distribution implied by quantum theory. Extensive experiments on the question answering dataset show that our method is comparable to the baselines and can explain why the selected question and answer sentence is the best option in terms of intra-sentence and inter-sentence attention distributions.