Recent years have witnessed a surge of interests of using neural topic models
for automatic topic extraction from text, since they avoid the complicated
mathematical derivations for model inference as in traditional topic models
such as Latent Dirichlet Allocation (LDA). However, these models either
typically assume improper prior (e.g. Gaussian or Logistic Normal) over latent
topic space or could not infer topic distribution for a given document. To
address these limitations, we propose a neural topic modeling approach, called
Bidirectional Adversarial Topic (BAT) model, which represents the first attempt
of applying bidirectional adversarial training for neural topic modeling. The
proposed BAT builds a two-way projection between the document-topic
distribution and the document-word distribution. It uses a generator to capture
the semantic patterns from texts and an encoder for topic inference.
Furthermore, to incorporate word relatedness information, the Bidirectional
Adversarial Topic model with Gaussian (Gaussian-BAT) is extended from BAT. To
verify the effectiveness of BAT and Gaussian-BAT, three benchmark corpora are
used in our experiments. The experimental results show that BAT and
Gaussian-BAT obtain more coherent topics, outperforming several competitive
baselines. Moreover, when performing text clustering based on the extracted
topics, our models outperform all the baselines, with more significant
improvements achieved by Gaussian-BAT where an increase of near 6\% is observed
in accuracy.

提出了一种用于文本中的主题抽取的神经主题建模方法，Bidirectional Adversarial Topic (BAT) 模型，该模型使用双向对抗训练构建文档 - 主题分布和文档 - 单词分布之间的两向投影，并扩展了 Gaussian-BAT 模型用以提高相关单词信息的综合利用。在三个基准数据集上进行的实验结果表明，BAT 和 Gaussian-BAT 能够获得更一致的主题，提升了文本聚类的准确性。

双向对抗训练的神经主题建模

Neural Topic Modeling with Bidirectional Adversarial Training

Topic models extract groups of words from documents, whose interpretation as
a topic hopefully allows for a better understanding of the data. However, the
resulting word groups are often not coherent, making them harder to interpret.
Recently, neural topic models have shown improvements in overall coherence.
Concurrently, contextual embeddings have advanced the state of the art of
neural models in general. In this paper, we combine contextualized
representations with neural topic models. We find that our approach produces
more meaningful and coherent topics than traditional bag-of-words topic models
and recent neural models. Our results indicate that future improvements in
language models will translate into better topic models.

本文介绍了关于主题模型的研究，探讨了如何将 contextual embeddings 应用于 neural topic models，以提高主题模型的准确性和一致性，并且研究结果表明，对自然语言处理模型的改进将会对主题模型产生积极影响。