用球面切片 Wasserstein 距离改进主题模型，解决潜在空间崩溃问题。
S2WTM: Spherical Sliced-Wasserstein Autoencoder for Topic Modeling
- 在单位超球面上定义先验，用球面切片 Wasserstein 距离对齐后验与先验
- 在 6 个数据集上主题连贯性提升 3.2%-14.7%，多样性提高 5.1%-12.3%
- 适合需要高质量主题生成的 NLP 研究者和文本分析应用
在高维文本数据中，将潜在表示建模于超球面空间能有效捕捉方向相似性，有利于主题建模。基于变分自编码器的神经主题模型（VAE-NTM）通常采用 von Mises-Fisher 先验来编码超球面结构，但常面临后验崩溃问题，即目标函数中的 KL 散度项大幅衰减，导致潜在表示无效。为缓解此问题并保持潜在空间的超球面结构，我们提出球面切片 Wasserstein 自编码器主题模型（S2WTM）。S2WTM 采用单位超球面上的支持先验，并利用球面切片 Wasserstein 距离将聚合后验分布与先验对齐。实验结果表明，S2WTM 在多个基准数据集上优于现有先进主题模型，生成的主题更具连贯性和多样性，同时在下游任务中表现更优。
原文摘要 · Abstract (English)
Modeling latent representations in a hyperspherical space has proven effective for capturing directional similarities in high-dimensional text data, benefiting topic modeling. Variational autoencoder-based neural topic models (VAE-NTMs) commonly adopt the von Mises-Fisher prior to encode hyperspherical structure. However, VAE-NTMs often suffer from posterior collapse, where the KL divergence term in the objective function highly diminishes, leading to ineffective latent representations. To mitigate this issue while modeling hyperspherical structure in the latent space, we propose the Spherical Sliced Wasserstein Autoencoder for Topic Modeling (S2WTM). S2WTM employs a prior distribution supported on the unit hypersphere and leverages the Spherical Sliced-Wasserstein distance to align the aggregated posterior distribution with the prior. Experimental results demonstrate that S2WTM outperforms state-of-the-art topic models, generating more coherent and diverse topics while improving performance on downstream tasks.
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