Zero-Shot Learners for Natural Language Understanding via a Unified Multiple-Choice Perspective

Zero-Shot Learners for Natural Language Understanding via a Unified Multiple-Choice Perspective

Zero-shot learning is an approach where models generalize to unseen tasks without direct training on them. We introduce the Unified Multiple-Choice (UniMC) framework, which is format-independent, compatible with various formats, and applicable to tasks like text classification and sentiment analysis...

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Bibliographic Details
Main Authors:	Junjie Wang, Ping Yang, Ruyi Gan, Yuxiang Zhang, Jiaxing Zhang, Tetsuya Sakai
Format:	Article
Language:	English
Published:	IEEE 2023-01-01
Series:	IEEE Access
Subjects:	Multi-task learning natural language understanding zero-shot learning
Online Access:	https://ieeexplore.ieee.org/document/10359522/

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