Papers by Houjin Yu

1 papers
Dual Context-Guided Continuous Prompt Tuning for Few-Shot Learning (2022.findings-acl)

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Challenge: Existing prompt-based paradigms have shown their competitive performance in many NLP tasks, but their effectiveness varies upon the model and training data.
Approach: They propose a dual context-guided continuous prompt tuning method that integrates contextual information into the input input.
Outcome: The proposed method outperforms existing prompt tuning methods in the few-shot setting and can be used in many NLP tasks.

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