Sentence Embedding Generation Framework Based on Kullback–Leibler Divergence Optimization and RoBERTa Knowledge Distillation

In natural language processing (NLP) tasks, computing semantic textual similarity (STS) is crucial for capturing nuanced semantic differences in text. Traditional word vector methods, such as Word2Vec and GloVe, as well as deep learning models like BERT, face limitations in handling context dependen...

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Autori principali:	Jin Han, Liang Yang
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	MDPI AG 2024-12-01
Serie:	Mathematics
Soggetti:	semantic textual similarity (STS) Kullback–Leibler divergence (KLD) knowledge distillation feature selection similarity evaluation sentence embedding
Accesso online:	https://www.mdpi.com/2227-7390/12/24/3990
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