More Discriminative Sentence Embeddings via Semantic Graph Smoothing

Autor:	Fettal, Chakib, Labiod, Lazhar, Nadif, Mohamed
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This paper explores an empirical approach to learn more discriminantive sentence representations in an unsupervised fashion. Leveraging semantic graph smoothing, we enhance sentence embeddings obtained from pretrained models to improve results for the text clustering and classification tasks. Our method, validated on eight benchmarks, demonstrates consistent improvements, showcasing the potential of semantic graph smoothing in improving sentence embeddings for the supervised and unsupervised document categorization tasks. Comment: Accepted in EACL 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.12890 Zobrazit plný text záznamu View this record from Arxiv