Convolutional Lie Operator for Sentence Classification

• URL: http://arxiv.org/abs/2512.16125v1
• Date: Thu, 18 Dec 2025 03:23:37 GMT
• Title: Convolutional Lie Operator for Sentence Classification
• Abstract summary: We integrate Lie Convolutions into Convolutional-based sentence classifiers, inspired by the ability of Lie group operations to capture complex, non-Euclidean symmetries.<n>Our proposed models SCLie and DPCLie empirically outperform traditional Convolutional-based sentence classifiers, suggesting that Lie-based models relatively improve the accuracy by capturing transformations not commonly associated with language.
• Score: 1.0026496861838448
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Traditional Convolutional Neural Networks have been successful in capturing local, position-invariant features in text, but their capacity to model complex transformation within language can be further explored. In this work, we explore a novel approach by integrating Lie Convolutions into Convolutional-based sentence classifiers, inspired by the ability of Lie group operations to capture complex, non-Euclidean symmetries. Our proposed models SCLie and DPCLie empirically outperform traditional Convolutional-based sentence classifiers, suggesting that Lie-based models relatively improve the accuracy by capturing transformations not commonly associated with language. Our findings motivate more exploration of new paradigms in language modeling.

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