Topic models have evolved from conventional Bayesian probabilistic models to recent Neural Topic Models (NTMs). Although NTMs have shown promising performance when trained and tested on a specific corpus, their generalisation ability across corpora has yet to be studied. In practice, we often expect that an NTM trained on a source corpus can still produce quality topical representation (i.e., latent distribution over topics) for the document from different target corpora. In this work, we aim to improve NTMs further so that their representation power for documents generalises reliably across corpora and tasks. To do so, we propose to enhance NTMs by narrowing the semantical distance between similar documents, with the underlying assumption that documents from different corpora may share similar semantics. Specifically, we obtain a similar document for each training document by text data augmentation. Then, we optimise NTMs further by minimising the semantical distance between each pair, measured by the Hierarchical Topic Transport Distance, which computes the Optimal Transport (OT) distance between their topical representations. Our framework can be readily applied to most NTMs as a plug-and-play module. Extensive experiments show that our framework significantly improves the generalisation ability regarding neural topical representation across corpora. Our code and datasets are available at: https://github.com/Xiaohao-Yang/Topic_Model_Generalisation

翻译：主题模型已从传统的贝叶斯概率模型发展到最近的神经主题模型（NTM）。尽管NTM在特定语料库上训练和测试时表现出色，但其跨语料库的泛化能力尚未得到充分研究。在实践中，我们通常期望在源语料库上训练的NTM仍能为来自不同目标语料库的文档生成高质量的主题表示（即文档在主题上的潜在分布）。本文旨在进一步改进NTM，使其对文档的表示能力能够稳定地泛化到不同语料库和任务中。为此，我们提出通过缩小相似文档之间的语义距离来增强NTM，这一方法的潜在假设是不同语料库中的文档可能共享相似的语义。具体而言，我们通过文本数据增强为每个训练文档获取一个相似文档，然后通过最小化每对文档之间的语义距离（该距离由分层主题传输距离（Hierarchical Topic Transport Distance）度量，计算其主题表示之间的最优传输（OT）距离）来进一步优化NTM。我们的框架可作为即插即用模块轻松应用于大多数NTM。大量实验表明，该框架显著提升了神经主题表示在跨语料库上的泛化能力。我们的代码和数据集可在以下网址获取：https://github.com/Xiaohao-Yang/Topic_Model_Generalisation