8 citations · 15 across the 7 of their papers we have counts for
7 papers
From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework
Yangyi Chen, Hongcheng Gao, Ganqu Cui +10
Textual adversarial attacks can discover models' weaknesses by adding semantic-preserved but misleading perturbations to the inputs. The long-lasting adversarial attack-and-defense…
Decoder Tuning: Efficient Language Understanding as Decoding
Ganqu Cui, Wentao Li, Ning Ding +3
With the evergrowing sizes of pre-trained models (PTMs), it has been an emerging practice to only provide the inference APIs for users, namely model-as-a-service (MaaS) setting. To…
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…
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…
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…
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…
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…