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Abstract Neural topic modeling is a scalable automated technique for text data mining. In various downstream tasks of topic modeling, it is preferred that the discovered topics well align with labels. However, due to the lack of guidance from labels, unsupervised neural topic models are less powerful in this situation. Existing supervised neural topic models often adopt a label-free prior to generate the latent document-topic distributions and use them to predict the labels and thus achieve label-topic alignment indirectly. Such a mechanism faces the following issues: 1) The label-free prior leads to topics blending the latent patterns of multiple labels; and 2) One is unable to intuitively identify the explicit relationships between labels and the discovered topics. To tackle these problems, we develop a novel supervised neural topic model which utilizes a chain-structured graphical model with a label-conditioned prior. Soft indicators are introduced to explicitly construct the label-topic relationships. To obtain well-organized label-topic relationships, we formalize an entropy-regularized optimal transport problem on the embedding space and model them as the transport plan. Moreover, our proposed method can be flexibly integrated with most existing unsupervised neural topic models. Experimental results on multiple datasets demonstrate that our model can greatly enhance the alignment between labels and topics while maintaining good topic quality.
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Improving Neural Topic Models using Knowledge Distillation
Abstract: Topic models are often used to identify human-interpretable topics to help make sense of large document collections. We use knowledge distillation to combine the best attributes of probabilistic topic models and pretrained transformers. Our modular method can be straightforwardly applied with any neural topic model to improve topic quality, which we demonstrate using two models having disparate architectures, obtaining state-of-the-art topic coherence. We show that our adaptable framework not only improves performance in the aggregate over all estimated topics, as is commonly reported, but also in head-to-head comparisons of aligned topics.
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- Award ID(s):
- 2031736
- PAR ID:
- 10225167
- Date Published:
- Journal Name:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Page Range / eLocation ID:
- 1752 to 1771
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.18653/v1/2020.emnlp-main.137
