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Status: Unchecked ×Keyword: sentence embeddings ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations

   Giorgi, Nitski, Wang and Bader · arXiv (Cornell University) · 2020

   The paper presents DeCLUTR, a self-supervised contrastive method for learning universal sentence embeddings without labelled data, and reports that it matches supervised approaches when extending transformer pretraining.

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   1. UncheckedUsed to extend pretraining of transformer language models, DeCLUTR is said to close the gap between unsupervised and supervised sentence encoders.“When used to extend the pretraining of transformer-based language models, our approach closes the performance gap between unsupervised and supervised pretraining for universal sentence encoders.”
   2. UncheckedIn the authors' experiments, sentence embedding quality appears to improve with both model size and the amount of unlabelled training text.“Importantly, our experiments suggest that the quality of the learned embeddings scale with both the number of trainable parameters and the amount of unlabelled training data.”