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Found 7 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.
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.
