Abstract
Topic models are evaluated based on their ability to describe documents well (i.e. low perplexity) and to produce topics that carry coherent semantic meaning. In topic modeling so far, perplexity is a direct optimization target. However, topic coherence, owing to its challenging computation, is not optimized for and is only evaluated after training. In this work, under a neural variational inference framework, we propose methods to incorporate a topic coherence objective into the training process. We demonstrate that such a coherence-aware topic model exhibits a similar level of perplexity as baseline models but achieves substantially higher topic coherence.- Anthology ID:
- D18-1096
- Volume:
- Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
- Month:
- October-November
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 830–836
- Language:
- URL:
- https://aclanthology.org/D18-1096
- DOI:
- 10.18653/v1/D18-1096
- Cite (ACL):
- Ran Ding, Ramesh Nallapati, and Bing Xiang. 2018. Coherence-Aware Neural Topic Modeling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 830–836, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Coherence-Aware Neural Topic Modeling (Ding et al., EMNLP 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/D18-1096.pdf
- Code
- additional community code