most citedPrototypical Verbalizer for Prompt-based Few-shot Tuning

8 citations · 15 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2022

Text Editing as Imitation Game

Ning Shi, Bin Tang, Bo Yuan +4

Text editing, such as grammatical error correction, arises naturally from imperfect textual data. Recent works frame text editing as a multi-round sequence tagging task, where oper…

cs.CL2022

Syntax-guided Localized Self-attention by Constituency Syntactic Distance

Shengyuan Hou, Jushi Kai, Haotian Xue +5

Recent works have revealed that Transformers are implicitly learning the syntactic information in its lower layers from data, albeit is highly dependent on the quality and scale of…

cs.CL20222 cited

Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP

Yangyi Chen, Hongcheng Gao, Ganqu Cui +4

Textual adversarial samples play important roles in multiple subfields of NLP research, including security, evaluation, explainability, and data augmentation. However, most work mi…

cs.AI20225 cited

Supervised Prototypical Contrastive Learning for Emotion Recognition in Conversation

Xiaohui Song, Longtao Huang, Hui Xue +1

Capturing emotions within a conversation plays an essential role in modern dialogue systems. However, the weak correlation between emotions and semantics brings many challenges to…

cs.CL20228 cited

Prototypical Verbalizer for Prompt-based Few-shot Tuning

Ganqu Cui, Shengding Hu, Ning Ding +2

Prompt-based tuning for pre-trained language models (PLMs) has shown its effectiveness in few-shot learning. Typically, prompt-based tuning wraps the input text into a cloze questi…