1 code implementation • • Guangtao Zeng, Wenmian Yang, Zeqian Ju, Yue Yang, Sicheng Wang, Ruisi Zhang, Meng Zhou, Jiaqi Zeng, Xiangyu Dong, Ruoyu Zhang, Hongchao Fang, Penghui Zhu, Shu Chen, Pengtao Xie
We also study the transferability of models trained on MedDialog to low-resource medical dialogue generation tasks.
Motivated by this, we propose Rayleigh Quotient Graph Neural Network (RQGNN), the first spectral GNN for graph-level anomaly detection, providing a new perspective on exploring the inherent spectral features of anomalous graphs.
Text generation from semantic parses is to generate textual descriptions for formal representation inputs such as logic forms and SQL queries.
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.
Our model has a multi-step decoder that injects the entity types into the process of entity mention generation.
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.
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.