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Lecture notes in computer science

Context Based Quantum Language Model with Application to Question Answering

Qin Zhao, Chenguang Hou, Ruifeng Xu

Published Jan 1, 2020• 1 citation
Publisher
DOI
10.1007/978-3-030-59585-2_3

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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.
We introduce quantum natural language processing for the Chinese language. Our approach focuses on Question-answering on a set of sentences, whether a particular sentence is truthful with respect to the whole corpus of sentences. We employ the Categorical Distributional Compositional (DisCoCat) model to translate sentences to valid quantum circuits. We achieve a fitting score of 97% on the test set. Our sentence set is also significantly larger than previous experiments. The results show general applicability of the framework to other languages. We also show that it can be used to introspect natural language models and provide new approaches to model explainability.
Visual Question Answering (VQA) systems are artificial intelligence applications that perform multimodal inference by combining visual and linguistic information. This review surveys the current literature on integrating quantum computing, quantum machine learning (QML), and quantum natural language processing (QNLP) techniques in VQA systems. It also discusses the potential impact of quantum computing methods on VQA applications in the context of 6G networks. The paper will demonstrate how this integration can contribute to developing more robust, secure, and efficient VQA solutions.