论文标题
一个分散的对抗性神经主题模型，用于将意见与用户评论中的情节分开
A Disentangled Adversarial Neural Topic Model for Separating Opinions from Plots in User Reviews
论文作者
论文摘要
变异自动编码器（VAE）中推断过程的灵活性最近导致修订了传统的概率主题模型，从而引起了神经主题模型（NTMS）。尽管这些方法取得了显着的结果，但令人惊讶的是，如何在如何解开潜在主题方面做得很少。现有的主题模型应用于评论时可能会提取与作家的主观意见相关的主题，并将与事实描述有关的主观观点（例如电影和书评中的情节摘要）提取。因此，希望自动将意见主题与情节/中性主题分开，从而可以更好地解释。在本文中，我们提出了一个神经主题模型以及对抗性训练，以将观点主题与情节和中性主题相关联。我们进行了广泛的实验评估，介绍了新的电影和书评及其情节的集合，即Mobo数据集，显示了提高的连贯性和各种主题，一致的分离率以及情感分类表现优于其他受监督的主题模型。
The flexibility of the inference process in Variational Autoencoders (VAEs) has recently led to revising traditional probabilistic topic models giving rise to Neural Topic Models (NTMs). Although these approaches have achieved significant results, surprisingly very little work has been done on how to disentangle the latent topics. Existing topic models when applied to reviews may extract topics associated with writers' subjective opinions mixed with those related to factual descriptions such as plot summaries in movie and book reviews. It is thus desirable to automatically separate opinion topics from plot/neutral ones enabling a better interpretability. In this paper, we propose a neural topic model combined with adversarial training to disentangle opinion topics from plot and neutral ones. We conduct an extensive experimental assessment introducing a new collection of movie and book reviews paired with their plots, namely MOBO dataset, showing an improved coherence and variety of topics, a consistent disentanglement rate, and sentiment classification performance superior to other supervised topic models.
