Dingyi Zeng
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View article: Leveraging Asynchronous Spiking Neural Networks for Ultra Efficient Event-Based Visual Processing
Leveraging Asynchronous Spiking Neural Networks for Ultra Efficient Event-Based Visual Processing Open
Event cameras encode visual information by generating asynchronous and sparse event streams, which hold great potential for low latency and low power consumption. Despite many successful implementations of event camera-based applications, …
View article: Mixed-Precision Graph Neural Quantization for Low Bit Large Language Models
Mixed-Precision Graph Neural Quantization for Low Bit Large Language Models Open
Post-Training Quantization (PTQ) is pivotal for deploying large language models (LLMs) within resource-limited settings by significantly reducing resource demands. However, existing PTQ strategies underperform at low bit levels < 3 bits du…
View article: A Compressive Memory-based Retrieval Approach for Event Argument Extraction
A Compressive Memory-based Retrieval Approach for Event Argument Extraction Open
Recent works have demonstrated the effectiveness of retrieval augmentation in the Event Argument Extraction (EAE) task. However, existing retrieval-based EAE methods have two main limitations: (1) input length constraints and (2) the gap b…
View article: Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction Open
Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To address these limitations, here we propose a multiple-event arg…
View article: MLPs Compass: What is learned when MLPs are combined with PLMs?
MLPs Compass: What is learned when MLPs are combined with PLMs? Open
While Transformer-based pre-trained language models and their variants exhibit strong semantic representation capabilities, the question of comprehending the information gain derived from the additional components of PLMs remains an open q…
View article: Document-Level Relation Extraction with Structural Encoding And Entity-Pair-Level Information Interaction
Document-Level Relation Extraction with Structural Encoding And Entity-Pair-Level Information Interaction Open
View article: Rethinking Relation Classification with Graph Meaning Representations
Rethinking Relation Classification with Graph Meaning Representations Open
View article: Rethinking Relation Classification with Graph Meaning Representations
Rethinking Relation Classification with Graph Meaning Representations Open
In the field of natural language understanding, the intersection of neural models and graph meaning representations (GMRs) remains a compelling area of research. Despite the growing interest, a critical gap persists in understanding the ex…
View article: Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction
Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction Open
Document-level event argument extraction is a crucial yet challenging task within the field of information extraction. Current mainstream approaches primarily focus on the information interaction between event triggers and their arguments,…
View article: Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance
Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance Open
Document-level event argument extraction poses new challenges of long input and cross-sentence inference compared to its sentence-level counterpart. However, most prior works focus on capturing the relations between candidate arguments and…
View article: Substructure Aware Graph Neural Networks
Substructure Aware Graph Neural Networks Open
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning, conventional GNNs struggle to break through the upper limit of the expressiveness of first-order Weisfeiler-Leman graph isomorphism test algorithm (1-WL) due…
View article: Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance
Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance Open
Document-level event argument extraction poses new challenges of long input and cross-sentence inference compared to its sentence-level counterpart. However, most prior works focus on capturing the relations between candidate arguments and…
View article: DPGNN: Dual-Perception Graph Neural Network for Representation Learning
DPGNN: Dual-Perception Graph Neural Network for Representation Learning Open
Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning on graphs. However, most existing GNNs are based on the mess…