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Keyword: domain shift ×Clear all

1 claim from 1 paper

1. Computer Science › Topic Modeling

   Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learning

   Jiang, Zhang and Wang · 2022

   The paper proposes PromCSE, which trains only a small soft prompt on a fixed language model and adds an energy-based hinge loss, to improve sentence embeddings and their robustness to domain shift.

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   1. UncheckedPromCSE is reported to beat current state-of-the-art sentence embedding models on seven standard similarity tasks and one domain-shifted task.“Empirical results on seven standard semantic textual similarity (STS) tasks and a domain-shifted STS task both show the effectiveness of our method compared with the current state-of-the-art sentence embedding models.”