DeepAI AI Chat
Log In Sign Up

Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation

12/22/2020
by   Shuai Lin, et al.
0

Human doctors with well-structured medical knowledge can diagnose a disease merely via a few conversations with patients about symptoms. In contrast, existing knowledge-grounded dialogue systems often require a large number of dialogue instances to learn as they fail to capture the correlations between different diseases and neglect the diagnostic experience shared among them. To address this issue, we propose a more natural and practical paradigm, i.e., low-resource medical dialogue generation, which can transfer the diagnostic experience from source diseases to target ones with a handful of data for adaptation. It is capitalized on a commonsense knowledge graph to characterize the prior disease-symptom relations. Besides, we develop a Graph-Evolving Meta-Learning (GEML) framework that learns to evolve the commonsense graph for reasoning disease-symptom correlations in a new disease, which effectively alleviates the needs of a large number of dialogues. More importantly, by dynamically evolving disease-symptom graphs, GEML also well addresses the real-world challenges that the disease-symptom correlations of each disease may vary or evolve along with more diagnostic cases. Extensive experiment results on the CMDD dataset and our newly-collected Chunyu dataset testify the superiority of our approach over state-of-the-art approaches. Besides, our GEML can generate an enriched dialogue-sensitive knowledge graph in an online manner, which could benefit other tasks grounded on knowledge graph.

READ FULL TEXT

page 1

page 2

page 3

page 4

04/19/2020

Knowledge-graph based Proactive Dialogue Generation with Improved Meta-Learning

Knowledge graph-based dialogue systems can narrow down knowledge candida...
01/30/2019

End-to-End Knowledge-Routed Relational Dialogue System for Automatic Diagnosis

Beyond current conversational chatbots or task-oriented dialogue systems...
04/19/2020

Dynamic Knowledge Graph-based Dialogue Generation with Improved Adversarial Meta-Learning

Knowledge graph-based dialogue systems are capable of generating more in...
10/17/2019

Using a KG-Copy Network for Non-Goal Oriented Dialogues

Non-goal oriented, generative dialogue systems lack the ability to gener...
04/24/2017

Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings

We study a symmetric collaborative dialogue setting in which two agents,...
09/09/2021

A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation

Neural conversation models have shown great potentials towards generatin...
10/04/2020

Dialogue Generation on Infrequent Sentence Functions via Structured Meta-Learning

Sentence function is an important linguistic feature indicating the comm...