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The Tenth International Conference on Learning Representations (ICLR-22), 2022
We define a challenging yet practical problem as Lifelong Few-shot Language Learning and propose a unified framework for it based on prompt tuning of T5.
Citation: Chengwei Qin and Shafiq Joty. LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5. In the Tenth International Conference on Learning Representations (ICLR-22).
Paper Link: https://openreview.net/forum?id=HCRVf71PMF
The 60th Annual Meeting of the Association for Computational Linguistics (ACL-22), 2022
Our paper introduces continual few-shot relation learning (CFRL), a challenging yet practical problem and proposes a novel method for this problem that outperforms existing approaches.
Citation: Chengwei Qin and Shafiq Joty. Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation. In the 60th Annual Meeting of the Association for Computational Linguistics (ACL-22 Oral).
Paper Link: https://openreview.net/forum?id=tN-UlSrCBgM
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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