Deep Dirichlet Multinomial Regression

Adrian Benton, Mark Dredze


Abstract
Dirichlet Multinomial Regression (DMR) and other supervised topic models can incorporate arbitrary document-level features to inform topic priors. However, their ability to model corpora are limited by the representation and selection of these features – a choice the topic modeler must make. Instead, we seek models that can learn the feature representations upon which to condition topic selection. We present deep Dirichlet Multinomial Regression (dDMR), a generative topic model that simultaneously learns document feature representations and topics. We evaluate dDMR on three datasets: New York Times articles with fine-grained tags, Amazon product reviews with product images, and Reddit posts with subreddit identity. dDMR learns representations that outperform DMR and LDA according to heldout perplexity and are more effective at downstream predictive tasks as the number of topics grows. Additionally, human subjects judge dDMR topics as being more representative of associated document features. Finally, we find that supervision leads to faster convergence as compared to an LDA baseline and that dDMR’s model fit is less sensitive to training parameters than DMR.
Anthology ID:
N18-1034
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
365–374
Language:
URL:
https://aclanthology.org/N18-1034
DOI:
10.18653/v1/N18-1034
Bibkey:
Cite (ACL):
Adrian Benton and Mark Dredze. 2018. Deep Dirichlet Multinomial Regression. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 365–374, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Deep Dirichlet Multinomial Regression (Benton & Dredze, NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1034.pdf
Video:
 http://vimeo.com/276397933
Code
 abenton/deep-dmr