Parameter-efficient feature-based transfer for paraphrase identification

There are many types of approaches for Paraphrase Identification (PI), an NLP task of determining whether a sentence pair has equivalent semantics. Traditional approaches mainly consist of unsupervised learning and feature engineering, which are computationally inexpensive. However, their task perfo...

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Bibliographic Details
Published in:	Natural Language Engineering Vol. 29; no. 4; pp. 1066 - 1096
Main Authors:	Liu, Xiaodong, Rzepka, Rafal, Araki, Kenji
Format:	Journal Article
Language:	English
Published:	Cambridge, UK Cambridge University Press 01.07.2023 Cambridge University Press (CUP)
Subjects:	Classifiers Computing costs Continual learning Datasets Identification Inference Language Learning Machine learning Natural language Natural language inference Natural language processing Neural networks Parameter identification Parameter-efficient feature-based transfer Paraphrase Paraphrase identification Semantic textual similarity Semantics Task performance Unsupervised learning Parameter-efficient feature-based transfer Natural language inference Paraphrase identification Semantic textual similarity Continual learning
ISSN:	1351-3249, 1469-8110
Online Access:	Get full text
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