1 citations · 3 across the 5 of their papers we have counts for
5 papers
Recouple Event Field via Probabilistic Bias for Event Extraction
Xingyu Bai, Taiqiang Wu, Han Guo +7
Event Extraction (EE), aiming to identify and classify event triggers and arguments from event mentions, has benefited from pre-trained language models (PLMs). However, existing PL…
Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs
Taiqiang Wu, Zhe Zhao, Jiahao Wang +4
Distilling high-accuracy Graph Neural Networks (GNNs) to low-latency multilayer perceptions (MLPs) on graph tasks has become a hot research topic. However, conventional MLP learnin…
SynGen: A Syntactic Plug-and-play Module for Generative Aspect-based Sentiment Analysis
Chengze Yu, Taiqiang Wu, Jiayi Li +2
Aspect-based Sentiment Analysis (ABSA) is a sentiment analysis task at fine-grained level. Recently, generative frameworks have attracted increasing attention in ABSA due to their…
Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval
Taiqiang Wu, Xingyu Bai, Weigang Guo +3
Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods…
Multi-stage Distillation Framework for Cross-Lingual Semantic Similarity Matching
Kunbo Ding, Weijie Liu, Yuejian Fang +3
Previous studies have proved that cross-lingual knowledge distillation can significantly improve the performance of pre-trained models for cross-lingual similarity matching tasks.…