Le Tian


Dual Context-Guided Continuous Prompt Tuning for Few-Shot Learning
Jie Zhou | Le Tian | Houjin Yu | Zhou Xiao | Hui Su | Jie Zhou
Findings of the Association for Computational Linguistics: ACL 2022

Prompt-based paradigm has shown its competitive performance in many NLP tasks. However, its success heavily depends on prompt design, and the effectiveness varies upon the model and training data. In this paper, we propose a novel dual context-guided continuous prompt (DCCP) tuning method. To explore the rich contextual information in language structure and close the gap between discrete prompt tuning and continuous prompt tuning, DCCP introduces two auxiliary training objectives and constructs input in a pair-wise fashion.Experimental results demonstrate that our method is applicable to many NLP tasks, and can often outperform existing prompt tuning methods by a large margin in the few-shot setting.


Deep LSTM based Feature Mapping for Query Classification
Yangyang Shi | Kaisheng Yao | Le Tian | Daxin Jiang
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies


Latent Semantic Tensor Indexing for Community-based Question Answering
Xipeng Qiu | Le Tian | Xuanjing Huang
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)