Self-Discriminative Learning for Unsupervised Document Embedding

ACL 2019

Self-Discriminative Learning for Unsupervised Document Embedding

Mar 24, 2021
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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. Authors: Hong-You Chen, Chin-Hua Hu, Leila Wehbe, Shou-De Lin (National Taiwan University, Carnegie Mellon University)

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