Search Results for author:
Found 8 papers, 3 papers with code
Recouple Event Field via Probabilistic Bias for Event Extraction
Event Extraction (EE), aiming to identify and classify event triggers and arguments from event mentions, has benefited from pre-trained language models (PLMs).
Weight-Inherited Distillation for Task-Agnostic BERT Compression
Knowledge Distillation (KD) is a predominant approach for BERT compression.
RIFormer: Keep Your Vision Backbone Effective While Removing Token Mixer
Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy.
Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPs
Distilling high-accuracy Graph Neural Networks~(GNNs) to low-latency multilayer perceptrons~(MLPs) on graph tasks has become a hot research topic.
SynGen: A Syntactic Plug-and-play Module for Generative Aspect-based Sentiment Analysis
To the best of our knowledge, we are the first one to introduce syntactic information to generative ABSA frameworks.
RIFormer: Keep Your Vision Backbone Effective but Removing Token Mixer
Extensive experiments and ablative analysis also demonstrate that the inductive bias of network architecture, can be incorporated into simple network structure with appropriate optimization strategy.
TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities
The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework.
Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval
We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph.
