Quantum-Inspired Complex-Valued Language Models for Aspect-Based Sentiment Classification |
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Authors: | Qin Zhao Chenguang Hou Ruifeng Xu |
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Affiliation: | 1.School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen 518055, China;2.Center for Remote Imaging, Sensing and Processing, National University of Singapore, Singapore 119076, Singapore;3.Peng Cheng Laboratory, Shenzhen 518066, China |
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Abstract: | Aiming at classifying the polarities over aspects, aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. The vector representations of current models are generally constrained to real values. Based on mathematical formulations of quantum theory, quantum language models have drawn increasing attention. Words in such models can be projected as physical particles in quantum systems, and naturally represented by representation-rich complex-valued vectors in a Hilbert Space, rather than real-valued ones. In this paper, the Hilbert Space representation for ABSA models is investigated and the complexification of three strong real-valued baselines are constructed. Experimental results demonstrate the effectiveness of complexification and the outperformance of our complex-valued models, illustrating that the complex-valued embedding can carry additional information beyond the real embedding. Especially, a complex-valued RoBERTa model outperforms or approaches the previous state-of-the-art on three standard benchmarking datasets. |
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Keywords: | quantum language model complexification aspect-based sentiment analysis |
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