Search Results for author:
Found 7 papers, 6 papers with code
CogDL: A Comprehensive Library for Graph Deep Learning
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
Cognitive Graph for Multi-Hop Reading Comprehension at Scale
We propose a new CogQA framework for multi-hop question answering in web-scale documents.
Ranked #50 on
Question Answering
on HotpotQA
Multi-hop Question Answering
Multi-Hop Reading Comprehension
+1
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training
Graph representation learning has emerged as a powerful technique for addressing real-world problems.
Towards Knowledge-Based Personalized Product Description Generation in E-commerce
In order to make the description both informative and personalized, KOBE considers a variety of important factors during text generation, including product aspects, user categories, and knowledge base, etc.
Towards Knowledge-Based Recommender Dialog System
In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System.
Ranked #5 on
Text Generation
on ReDial
VarCLR: Variable Semantic Representation Pre-training via Contrastive Learning
Machine learning-based program analysis methods use variable name representations for a wide range of tasks, such as suggesting new variable names and bug detection.
Local Clustering Graph Neural Networks
Based on the theoretical analysis, we propose Local Clustering Graph Neural Networks (LCGNN), a GNN learning paradigm that utilizes local clustering to efficiently search for small but compact subgraphs for GNN training and inference.
