Papers by Osvald Nitski

1 papers
DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations (2021.acl-long)

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Challenge: Sentence embeddings are an important component of many natural language processing systems.
Approach: They propose a self-supervised objective for learning universal sentence embeddings that does not require labelled training data.
Outcome: The proposed approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders.

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