Search Results for author:
Found 9 papers, 6 papers with code
MedDialog: Large-scale Medical Dialogue Datasets
We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks.
SmoothGNN: Smoothing-based GNN for Unsupervised Node Anomaly Detection
Through empirical and theoretical analysis of real-world node anomaly detection (NAD) datasets, we observe that anomalous and normal nodes show different patterns in the smoothing process, which can be leveraged to enhance NAD tasks.
KPG: Key Propagation Graph Generator for Rumor Detection based on Reinforcement Learning
In this paper, we propose Key Propagation Graph Generator (KPG), a novel reinforcement learning-based rumor detection framework that generates contextually coherent and informative propagation patterns for events with insufficient topology information, while also identifies indicative substructures for events with redundant and noisy propagation structures.
Rayleigh Quotient Graph Neural Networks for Graph-level Anomaly Detection
Moreover, we prove that the accumulated spectral energy of the graph signal can be represented by its Rayleigh Quotient, indicating that the Rayleigh Quotient is a driving factor behind the anomalous properties of graphs.
Logic-Consistency Text Generation from Semantic Parses
Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries.
Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL
Neural models have achieved significant results on the text-to-SQL task, in which most current work assumes all the input questions are legal and generates a SQL query for any input.
Injecting Entity Types into Entity-Guided Text Generation
Our model has a multi-step decoder that injects the entity types into the process of entity mention generation.
Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks
Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network.
MedDialog: Two Large-scale Medical Dialogue Datasets
Medical dialogue systems are promising in assisting in telemedicine to increase access to healthcare services, improve the quality of patient care, and reduce medical costs.
