NasH: Toward end-to-end neural architecture for generative semantic hashing

Published

Journal Article

© 2018 Association for Computational Linguistics 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 connections between proposed method and rate-distortion theory, which provides a theoretical foundation for the effectiveness of the proposed 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.

Full Text

Duke Authors

Cited Authors

  • Shen, D; Su, Q; Chapfuwa, P; Wang, W; Wang, G; Carin, L; Henao, R

Published Date

  • January 1, 2018

Published In

  • Acl 2018 56th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers)

Volume / Issue

  • 1 /

Start / End Page

  • 2041 - 2050

Citation Source

  • Scopus