17 citations · 19 across the 9 of their papers we have counts for
9 papers
Generative Modeling with Multi-Instance Reward Learning for E-commerce Creative Optimization
Qiaolei Gu, Yu Li, DingYi Zeng +6
In e-commerce advertising, selecting the most compelling combination of creative elements -- such as titles, images, and highlights -- is critical for capturing user attention and…
Mixed-Precision Graph Neural Quantization for Low Bit Large Language Models
Wanlong Liu, Yichen Xiao, Dingyi Zeng +3
Post-Training Quantization (PTQ) is pivotal for deploying large language models (LLMs) within resource-limited settings by significantly reducing resource demands. However, existin…
A Compressive Memory-based Retrieval Approach for Event Argument Extraction
Wanlong Liu, Enqi Zhang, Li Zhou +5
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…
Beyond Single-Event Extraction: Towards Efficient Document-Level Multi-Event Argument Extraction
Wanlong Liu, Li Zhou, Dingyi Zeng +6
Recent mainstream event argument extraction methods process each event in isolation, resulting in inefficient inference and ignoring the correlations among multiple events. To addr…
MLPs Compass: What is learned when MLPs are combined with PLMs?
Li Zhou, Wenyu Chen, Yong Cao +3
While Transformer-based pre-trained language models and their variants exhibit strong semantic representation capabilities, the question of comprehending the information gain deriv…
Enhancing Document-level Event Argument Extraction with Contextual Clues and Role Relevance
Wanlong Liu, Shaohuan Cheng, Dingyi Zeng +1
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…
Rethinking Relation Classification with Graph Meaning Representations
Li Zhou, Wenyu Chen, Dingyi Zeng +2
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 growin…
Utilizing Contextual Clues and Role Correlations for Enhancing Document-level Event Argument Extraction
Wanlong Liu, Dingyi Zeng, Li Zhou +6
Document-level event argument extraction is a crucial yet challenging task within the field of information extraction. Current mainstream approaches primarily focus on the informat…
DPGNN: Dual-Perception Graph Neural Network for Representation Learning
Li Zhou, Wenyu Chen, Dingyi Zeng +4
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 o…