Document Hashing with Mixture-Prior Generative Models

Wei Dong, Qinliang Su, Dinghan Shen, Changyou Chen

Abstract
Hashing is promising for large-scale information retrieval tasks thanks to the efficiency of distance evaluation between binary codes. Generative hashing is often used to generate hashing codes in an unsupervised way. However, existing generative hashing methods only considered the use of simple priors, like Gaussian and Bernoulli priors, which limits these methods to further improve their performance. In this paper, two mixture-prior generative models are proposed, under the objective to produce high-quality hashing codes for documents. Specifically, a Gaussian mixture prior is first imposed onto the variational auto-encoder (VAE), followed by a separate step to cast the continuous latent representation of VAE into binary code. To avoid the performance loss caused by the separate casting, a model using a Bernoulli mixture prior is further developed, in which an end-to-end training is admitted by resorting to the straight-through (ST) discrete gradient estimator. Experimental results on several benchmark datasets demonstrate that the proposed methods, especially the one using Bernoulli mixture priors, consistently outperform existing ones by a substantial margin.
Anthology ID:
D19-1526
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
5226–5235
Language:
URL:
https://aclanthology.org/D19-1526
DOI:
10.18653/v1/D19-1526
Bibkey:
Cite (ACL):
Wei Dong, Qinliang Su, Dinghan Shen, and Changyou Chen. 2019. Document Hashing with Mixture-Prior Generative Models. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5226–5235, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Document Hashing with Mixture-Prior Generative Models (Dong et al., EMNLP-IJCNLP 2019)
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PDF:
https://preview.aclanthology.org/starsem-semeval-split/D19-1526.pdf
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