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        <title>Self-Discriminative Learning for Unsupervised Document Embedding</title>
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        <namePart type="given">Hong-You</namePart>
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    <abstract>Unsupervised document representation learning is an important task providing pre-trained features for NLP applications. Unlike most previous work which learn the embedding based on self-prediction of the surface of text, we explicitly exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. Extensive experiments on both small and large public datasets show the competitiveness of the proposed method. In evaluations on standard document classification, our model has errors that are 5 to 13% lower than state-of-the-art unsupervised embedding models. The reduction in error is even more pronounced in scarce label setting.</abstract>
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%0 Conference Proceedings
%T Self-Discriminative Learning for Unsupervised Document Embedding
%A Chen, Hong-You
%A Hu, Chin-Hua
%A Wehbe, Leila
%A Lin, Shou-De
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F chen-etal-2019-self
%X Unsupervised document representation learning is an important task providing pre-trained features for NLP applications. Unlike most previous work which learn the embedding based on self-prediction of the surface of text, we explicitly exploit the inter-document information and directly model the relations of documents in embedding space with a discriminative network and a novel objective. Extensive experiments on both small and large public datasets show the competitiveness of the proposed method. In evaluations on standard document classification, our model has errors that are 5 to 13% lower than state-of-the-art unsupervised embedding models. The reduction in error is even more pronounced in scarce label setting.
%R 10.18653/v1/N19-1255
%U https://aclanthology.org/N19-1255
%U https://doi.org/10.18653/v1/N19-1255
%P 2465-2474

Markdown (Informal)

[Self-Discriminative Learning for Unsupervised Document Embedding](https://aclanthology.org/N19-1255) (Chen et al., NAACL 2019)

• Self-Discriminative Learning for Unsupervised Document Embedding (Chen et al., NAACL 2019)

ACL

• Hong-You Chen, Chin-Hua Hu, Leila Wehbe, and Shou-De Lin. 2019. Self-Discriminative Learning for Unsupervised Document Embedding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2465–2474, Minneapolis, Minnesota. Association for Computational Linguistics.