@inproceedings{be99c93da01046e9a34cd3bbf3812b72,
title = "Neural Mixed Counting Models for Dispersed Topic Discovery",
abstract = "Mixed counting models that use the negative binomial distribution as the prior can well model over-dispersed and hierarchically dependent random variables; thus they have attracted much attention in mining dispersed document topics. However, the existing parameter inference method like Monte Carlo sampling is quite time-consuming. In this paper, we propose two efficient neural mixed counting models, i.e., the Negative Binomial-Neural Topic Model (NB-NTM) and the Gamma Negative Binomial-Neural Topic Model (GNB-NTM) for dispersed topic discovery. Neural variational inference algorithms are developed to infer model parameters by using the reparameterization of Gamma distribution and the Gaussian approximation of Poisson distribution. Experiments on real-world datasets indicate that our models outperform state-of-the-art baseline models in terms of perplexity and topic coherence. The results also validate that both NB-NTM and GNB-NTM can produce explainable intermediate variables by generating dispersed proportions of document topics.",
author = "Jiemin WU and Yanghui RAO and Zusheng ZHANG and Haoran XIE and Qing LI and WANG, {Fu Lee} and Ziye CHEN",
note = "The first two authors contributed equally to this work which was finished when Jiemin Wu was an undergraduate student of his final year.; 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020 ; Conference date: 05-07-2020 Through 10-07-2020",
year = "2020",
month = jul,
language = "English",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "6159--6169",
editor = "Dan JURAFSKY and Joyce CHAI and Natalie SCHLUTER and Joel TETREAULT",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
address = "United States",
}