Coherence-Aware Neural Topic Modeling

Ran Ding, Ramesh Nallapati, Bing Xiang


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
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
830–836
Language:
URL:
https://www.aclweb.org/anthology/D18-1096
DOI:
10.18653/v1/D18-1096
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PDF:
http://aclanthology.lst.uni-saarland.de/D18-1096.pdf