NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing

Dinghan Shen, Qinliang Su, Paidamoyo Chapfuwa, Wenlin Wang, Guoyin Wang, Ricardo Henao, Lawrence Carin


Abstract
Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems. While fairly successful, previous techniques generally require two-stage training, and the binary constraints are handled ad-hoc. In this paper, we present an end-to-end Neural Architecture for Semantic Hashing (NASH), where the binary hashing codes are treated as Bernoulli latent variables. A neural variational inference framework is proposed for training, where gradients are directly backpropagated through the discrete latent variable to optimize the hash function. We also draw the connections between proposed method and rate-distortion theory, which provides a theoretical foundation for the effectiveness of our framework. Experimental results on three public datasets demonstrate that our method significantly outperforms several state-of-the-art models on both unsupervised and supervised scenarios.
Anthology ID:
P18-1190
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2041–2050
Language:
URL:
https://aclanthology.org/P18-1190
DOI:
10.18653/v1/P18-1190
Bibkey:
Cite (ACL):
Dinghan Shen, Qinliang Su, Paidamoyo Chapfuwa, Wenlin Wang, Guoyin Wang, Ricardo Henao, and Lawrence Carin. 2018. NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2041–2050, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing (Shen et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-1190.pdf
Presentation:
 P18-1190.Presentation.pdf
Video:
 https://vimeo.com/285805069