Papers with semi-supervised sentence embedding framework

1 papers
Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework (2022.emnlp-main)

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Challenge: Existing supervised sentence embedding techniques rely on expensive human-annotated sentence pairs as the supervised signals.
Approach: They propose a semi-supervised sentence embedding framework that leverages large-scale unlabeled data.
Outcome: The proposed framework surpasses state-of-the-art methods on four domain adaptation tasks.

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