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        <title>Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling</title>
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        <namePart type="given">Fanghua</namePart>
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    <abstract>Semantic hashing is a powerful paradigm for representing texts as compact binary hash codes. The explosion of short text data has spurred the demand of few-bits hashing. However, the performance of existing semantic hashing methods cannot be guaranteed when applied to few-bits hashing because of severe information loss. In this paper, we present a simple but effective unsupervised neural generative semantic hashing method with a focus on few-bits hashing. Our model is built upon variational autoencoder and represents each hash bit as a Bernoulli variable, which allows the model to be end-to-end trainable. To address the issue of information loss, we introduce a set of auxiliary implicit topic vectors. With the aid of these topic vectors, the generated hash codes are not only low-dimensional representations of the original texts but also capture their implicit topics. We conduct comprehensive experiments on four datasets. The results demonstrate that our approach achieves significant improvements over state-of-the-art semantic hashing methods in few-bits hashing.</abstract>
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%0 Conference Proceedings
%T Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling
%A Ye, Fanghua
%A Manotumruksa, Jarana
%A Yilmaz, Emine
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F ye-etal-2020-unsupervised
%X Semantic hashing is a powerful paradigm for representing texts as compact binary hash codes. The explosion of short text data has spurred the demand of few-bits hashing. However, the performance of existing semantic hashing methods cannot be guaranteed when applied to few-bits hashing because of severe information loss. In this paper, we present a simple but effective unsupervised neural generative semantic hashing method with a focus on few-bits hashing. Our model is built upon variational autoencoder and represents each hash bit as a Bernoulli variable, which allows the model to be end-to-end trainable. To address the issue of information loss, we introduce a set of auxiliary implicit topic vectors. With the aid of these topic vectors, the generated hash codes are not only low-dimensional representations of the original texts but also capture their implicit topics. We conduct comprehensive experiments on four datasets. The results demonstrate that our approach achieves significant improvements over state-of-the-art semantic hashing methods in few-bits hashing.
%R 10.18653/v1/2020.findings-emnlp.233
%U https://aclanthology.org/2020.findings-emnlp.233
%U https://doi.org/10.18653/v1/2020.findings-emnlp.233
%P 2566-2575

Markdown (Informal)

[Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling](https://aclanthology.org/2020.findings-emnlp.233) (Ye et al., Findings 2020)

• Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling (Ye et al., Findings 2020)

ACL

• Fanghua Ye, Jarana Manotumruksa, and Emine Yilmaz. 2020. Unsupervised Few-Bits Semantic Hashing with Implicit Topics Modeling. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2566–2575, Online. Association for Computational Linguistics.