Proceedings of the AAAI Conference on Artificial Intelligence

End-to-End Quantum-like Language Models with Application to Question Answering

Peng Zhang, Jiabin Niu, Zhan Su, Benyou Wang, Liqun Ma, Dawei Song

Published Apr 27, 2018• 86 citations

Abstract

Language Modeling (LM) is a fundamental research topic in a range of areas. Recently, inspired by quantum theory, a novel Quantum Language Model (QLM) has been proposed for Information Retrieval (IR). In this paper, we aim to broaden the theoretical and practical basis of QLM. We develop a Neural Network based Quantum-like Language Model (NNQLM) and apply it to Question Answering. Specifically, based on word embeddings, we design a new density matrix, which represents a sentence (e.g., a question or an answer) and encodes a mixture of semantic subspaces. Such a density matrix, together with a joint representation of the question and the answer, can be integrated into neural network architectures (e.g., 2-dimensional convolutional neural networks). Experiments on the TREC-QA and WIKIQA datasets have verified the effectiveness of our proposed models.

DOI: 10.1609/aaai.v32i1.11979

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