Context Based Quantum Language Model with Application to Question Answering

Qin Zhao, Chenguang Hou, Ruifeng Xu

Abstract: Equipped with quantum probability theory, quantum language models (QLMs) aimed at a principled approach to modeling term dependency have drawn increasing attention. However, even though they are theoretically more general and have effective performance, current QLMs do not take context information into account. The most important element, namely density matrix, is constructed as a summation of word projectors, whose representation is independent of context information. To address this problem, we propose a Context based Quantum Language Model (C-QLM). Between word embedding and sentence density matrix, a bidirectional long short term memory network is adopted to learn the hidden context information. Then a set of vectors is utilized to extract density matrices’s features for question and answer sentences which can be used to calculate the matching score. Experiment results on TREC-QA and WIKI-QA datasets demonstrate the effectiveness of our proposed model.