150 citations · 415 across the 37 of their papers we have counts for
7 papers · 2 filters
Sparse Teachers Can Be Dense with Knowledge
Yi Yang, Chen Zhang, Dawei Song
Recent advances in distilling pretrained language models have discovered that, besides the expressiveness of knowledge, the student-friendliness should be taken into consideration…
XPrompt: Exploring the Extreme of Prompt Tuning
Fang Ma, Chen Zhang, Lei Ren +5
Prompt tuning learns soft prompts to condition frozen Pre-trained Language Models (PLMs) for performing downstream tasks in a parameter-efficient manner. While prompt tuning has gr…
Structural Bias for Aspect Sentiment Triplet Extraction
Chen Zhang, Lei Ren, Fang Ma +3
Structural bias has recently been exploited for aspect sentiment triplet extraction (ASTE) and led to improved performance. On the other hand, it is recognized that explicitly inco…
Aspect-specific Context Modeling for Aspect-based Sentiment Analysis
Fang Ma, Chen Zhang, Bo Zhang +1
Aspect-based sentiment analysis (ABSA) aims at predicting sentiment polarity (SC) or extracting opinion span (OE) expressed towards a given aspect. Previous work in ABSA mostly rel…
Doge Tickets: Uncovering Domain-general Language Models by Playing Lottery Tickets
Yi Yang, Chen Zhang, Benyou Wang +1
Over-parameterized models, typically pretrained language models (LMs), have shown an appealing expressive power due to their small learning bias. However, the huge learning capacit…
MiniDisc: Minimal Distillation Schedule for Language Model Compression
Chen Zhang, Yang Yang, Qifan Wang +4
Recent studies have uncovered that language model distillation is less effective when facing a large capacity gap between the teacher and the student, and introduced teacher assist…
Making Pretrained Language Models Good Long-tailed Learners
Chen Zhang, Lei Ren, Jingang Wang +2
Prompt-tuning has shown appealing performance in few-shot classification by virtue of its capability in effectively exploiting pre-trained knowledge. This motivates us to check the…