June 11, 2024, 4:41 a.m. | Bowen Zhang, Chunping Li

cs.CL updates on arXiv.org arxiv.org

arXiv:2406.05326v1 Announce Type: new
Abstract: Since the introduction of BERT and RoBERTa, research on Semantic Textual Similarity (STS) has made groundbreaking progress. Particularly, the adoption of contrastive learning has substantially elevated state-of-the-art performance across various STS benchmarks. However, contrastive learning categorizes text pairs as either semantically similar or dissimilar, failing to leverage fine-grained annotated information and necessitating large batch sizes to prevent model collapse. These constraints pose challenges for researchers engaged in STS tasks that require nuanced similarity levels or …

abstract adoption art arxiv benchmarks bert cs.cl framework groundbreaking however introduction loss modeling performance progress regression relu research roberta semantic state text textual translated type

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