#5026. Quantum Probability-inspired Graph Attention Network for Modeling Complex Text Interaction

July 2026publication date
Proposal available till 25-05-2025
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Journal’s subject area:
Management Information Systems;
Information Systems and Management;
Software;
Artificial Intelligence;
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Abstract:
Inspired by quantum-like phenomena in human language understanding, recent studies propose quantum probability-inspired neural networks to model natural language by treating words as superposition states and a sentence as a mixed state. The existing quantum probability-inspired neural networks only encode sequential interaction of a sequence of words, but cannot model the complex interaction of text pieces. To generalize the quantum framework from modeling word sequence to modeling complex and graphical text interaction, we propose a Quantum Probability-inspired Graph Attention NeTwork (QPGAT) by combining quantum probability and graph attention mechanism in a unified framework. Then QPGAT models each text node as a particle in a superposition state and each nodes neighborhood in the graph as a mixed system in a mixed state to learn interaction-aware text node representations. Experiment results show that QPGAT is competitive compared with the state-of-the-art methods on the two complex NLP tasks, demonstrating the effectiveness of QPGAT.
Keywords:
Graph attention network; Natural language processing; Quantum probability

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