用新型卷积方法提升缅甸语文本分类，尤其在稀有语言识别上表现惊人。
KAConvText: Novel Approach to Burmese Sentence Classification using Kolmogorov-Arnold Convolution
- 引入柯尔莫哥洛夫-阿诺德卷积，结合fastText嵌入进行文本分类
- 在仇恨言论检测、新闻分类和民族语言识别中最高达99.82%准确率
- 使用KAN分类头增强可解释性，适合低资源语言研究者
本文首次将柯尔莫哥洛夫-阿诺德卷积（KAConvText）应用于句子分类，解决三项任务：不平衡二分类仇恨言论检测、平衡多分类新闻分类以及不平衡多分类民族语言识别。我们测试了多种嵌入配置，对比随机初始化与fastText嵌入在静态与微调设置下的表现，嵌入维度为100和300，采用CBOW与Skip-gram模型。基线包括标准CNN及融合柯尔莫哥洛夫-阿诺德网络（CNN-KAN）的CNN。此外，还尝试了不同分类头——MLP与KAN，其中使用KAN头可提升可解释性。结果表明，采用微调fastText嵌入的KAConvText-MLP在仇恨言论检测中达到91.23%准确率（F1=0.9109），新闻分类达92.66%准确率（F1=0.9267），语言识别高达99.82%准确率（F1=0.9982）。
原文摘要 · Abstract (English)
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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