KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution

• URL: http://arxiv.org/abs/2507.06753v1
• Date: Wed, 09 Jul 2025 11:25:35 GMT
• Title: KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution
• Authors: Ye Kyaw Thu, Thura Aung, Thazin Myint Oo, Thepchai Supnithi,
• Abstract summary: This paper presents the first application of Kolmogorov-Arnold Convolution for Text (KAConvText) in sentence classification.<n>We investigate various embedding configurations, comparing random to fastText embeddings in both static and fine-tuned settings.<n>Results show that KAConvText-MLP achieves the best performance of 9123% accuracy (F1-score = 0.9109) for hate speech detection, 92.66% accuracy (F1-score = 0.9267) for news classification, and 99.82% accuracy (F1-score = 0.9982) for language identification.
• Score: 0.16874375111244325
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper presents the first application of Kolmogorov-Arnold Convolution for Text (KAConvText) in sentence classification, addressing three tasks: imbalanced binary hate speech detection, balanced multiclass news classification, and imbalanced multiclass ethnic language identification. We investigate various embedding configurations, comparing random to fastText embeddings in both static and fine-tuned settings, with embedding dimensions of 100 and 300 using CBOW and Skip-gram models. Baselines include standard CNNs and CNNs augmented with a Kolmogorov-Arnold Network (CNN-KAN). In addition, we investigated KAConvText with different classification heads - MLP and KAN, where using KAN head supports enhanced interpretability. Results show that KAConvText-MLP with fine-tuned fastText embeddings achieves the best performance of 91.23% accuracy (F1-score = 0.9109) for hate speech detection, 92.66% accuracy (F1-score = 0.9267) for news classification, and 99.82% accuracy (F1-score = 0.9982) for language identification.

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