用扩散过程改进主题模型，提升文档主题分布的表达能力。
DiffETM: Diffusion Process Enhanced Embedded Topic Model
- 引入扩散过程优化文档-主题分布采样方式
- 在两个主流数据集上显著提升主题建模效果
- 适合关注主题建模性能提升的研究者
嵌入式主题模型（ETM）是一种广泛应用的方法，其假设文档-主题分布服从逻辑正态分布，以简化优化过程。然而，这一假设过于简化真实分布，限制了模型性能。为此，我们提出一种新方法，将扩散过程引入文档-主题分布的采样过程，克服该局限性的同时保持优化简便性。我们在两个主流数据集上进行了大量实验，验证了该方法在提升主题建模性能方面的有效性。
原文摘要 · Abstract (English)
The embedded topic model (ETM) is a widely used approach that assumes the sampled document-topic distribution conforms to the logistic normal distribution for easier optimization. However, this assumption oversimplifies the real document-topic distribution, limiting the model's performance. In response, we propose a novel method that introduces the diffusion process into the sampling process of document-topic distribution to overcome this limitation and maintain an easy optimization process. We validate our method through extensive experiments on two mainstream datasets, proving its effectiveness in improving topic modeling performance.
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