Convolutional Lie Operator for Sentence Classification

Daniela N. Rim; Heeyoul Choi

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arXiv · 2512.16125

Convolutional Lie Operator for Sentence Classification

Daniela N. Rim·Heeyoul Choi

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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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BibTeX RIS

Daniela N. Rim, Heeyoul Choi. 2025-12-18. Convolutional Lie Operator for Sentence Classification. https://arxiv.org/abs/2512.16125

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