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• W2950382646 abstract "While paragraph embedding models are remarkably effective for downstream classification tasks, what they learn and encode into a single vector remains opaque. In this paper, we investigate a state-of-the-art paragraph embedding method proposed by Zhang et al. (2017) and discover that it cannot reliably tell whether a given sentence occurs in the input paragraph or not. We formulate a sentence content task to probe for this basic linguistic property and find that even a much simpler bag-of-words method has no trouble solving it. This result motivates us to replace the reconstruction-based objective of Zhang et al. (2017) with our sentence content probe objective in a semi-supervised setting. Despite its simplicity, our objective improves over paragraph reconstruction in terms of (1) downstream classification accuracies on benchmark datasets, (2) faster training, and (3) better generalization ability." @default.
• W2950382646 created "2019-06-27" @default.
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• W2950382646 date "2019-01-01" @default.
• W2950382646 modified "2023-10-16" @default.
• W2950382646 title "Encouraging Paragraph Embeddings to Remember Sentence Identity Improves Classification" @default.
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• W2950382646 doi "https://doi.org/10.18653/v1/p19-1638" @default.
• W2950382646 hasPublicationYear "2019" @default.
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