Self-supervised pre-trained neural network for quantum natural language processing.

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Ben Yao, Prayag Tiwari, Qiuchi Li
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引用次数: 0

Abstract

Quantum computing models have propelled advances in many application domains. However, in the field of natural language processing (NLP), quantum computing models are limited in representation capacity due to the high linearity of the underlying quantum computing architecture. This work attempts to address this limitation by leveraging the concept of self-supervised pre-training, a paradigm that has been propelling the rocketing development of NLP, to increase the power of quantum NLP models on the representation level. Specifically, we present a self-supervised pre-training approach to train quantum encodings of sentences, and fine-tune quantum circuits for downstream tasks on its basis. Experiments show that pre-trained mechanism brings remarkable improvement over end-to-end pure quantum models, yielding meaningful prediction results on a variety of downstream text classification datasets.

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来源期刊
Neural Networks
Neural Networks 工程技术-计算机:人工智能
CiteScore
13.90
自引率
7.70%
发文量
425
审稿时长
67 days
期刊介绍: Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.
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