Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning-Reference-Cited by-同舟云学术

Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning

Published:2023-02-27 Issue: Volume: Page:
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Container-title:Proceedings of the Sixteenth ACM International Conference on Web Search and Data Mining
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Author:

Xu Ziyun¹^ORCID,Wang Chengyu²^ORCID,Qiu Minghui²^ORCID,Luo Fuli²^ORCID,Xu Runxin³^ORCID,Huang Songfang²^ORCID,Huang Jun²^ORCID

Affiliation:

1. Carnegie Mellon University, Pittsburgh, PA, USA

2. Alibaba Group, Hangzhou, China

3. Peking University, Beijing, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3539597.3570398

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4. Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL. 4171--4186.

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