Abstract
Advances on deep generative models have attracted significant research interest in neural topic modeling. The recently proposed Adversarial-neural Topic Model models topics with an adversarially trained generator network and employs Dirichlet prior to capture the semantic patterns in latent topics. It is effective in discovering coherent topics but unable to infer topic distributions for given documents or utilize available document labels. To overcome such limitations, we propose Topic Modeling with Cycle-consistent Adversarial Training (ToMCAT) and its supervised version sToMCAT. ToMCAT employs a generator network to interpret topics and an encoder network to infer document topics. Adversarial training and cycle-consistent constraints are used to encourage the generator and the encoder to produce realistic samples that coordinate with each other. sToMCAT extends ToMCAT by incorporating document labels into the topic modeling process to help discover more coherent topics. The effectiveness of the proposed models is evaluated on unsupervised/supervised topic modeling and text classification. The experimental results show that our models can produce both coherent and informative topics, outperforming a number of competitive baselines.- Anthology ID:
- 2020.emnlp-main.725
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9018–9030
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.725
- DOI:
- 10.18653/v1/2020.emnlp-main.725
- Cite (ACL):
- Xuemeng Hu, Rui Wang, Deyu Zhou, and Yuxuan Xiong. 2020. Neural Topic Modeling with Cycle-Consistent Adversarial Training. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9018–9030, Online. Association for Computational Linguistics.
- Cite (Informal):
- Neural Topic Modeling with Cycle-Consistent Adversarial Training (Hu et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.emnlp-main.725.pdf