The development of conversational agents to interact with patients and
deliver clinical advice has attracted the interest of many researchers,
particularly in light of the COVID-19 pandemic. The training of an end-to-end
neural based dialog system, on the other hand, is hampered by a lack of
multi-turn medical dialog corpus. We make the very first attempt to release a
high-quality multi-turn Medical Dialog dataset relating to Covid-19 disease
named CDialog, with over 1K conversations collected from the online medical
counselling websites. We annotate each utterance of the conversation with seven
different categories of medical entities, including diseases, symptoms, medical
tests, medical history, remedies, medications and other aspects as additional
labels. Finally, we propose a novel neural medical dialog system based on the
CDialog dataset to advance future research on developing automated medical
dialog systems. We use pre-trained language models for dialogue generation,
incorporating annotated medical entities, to generate a virtual doctor's
response that addresses the patient's query. Experimental results show that the
proposed dialog models perform comparably better when supplemented with entity
information and hence can improve the response quality.

本文介绍了 COVID-19 疾病的多轮医疗对话数据集 CDialog，包含超过 1,000 个对话，带有七个医疗实体类别的注释，利用预训练的语言模型和医疗实体信息提高了自动生成医疗对话模型的响应质量。

CDialog：针对实体感知的对话生成的多轮 Covid-19 对话数据集

CDialog: A Multi-turn Covid-19 Conversation Dataset for Entity-Aware Dialog Generation

For each goal-oriented dialog task of interest, large amounts of data need to
be collected for end-to-end learning of a neural dialog system. Collecting that
data is a costly and time-consuming process. Instead, we show that we can use
only a small amount of data, supplemented with data from a related dialog task.
Naively learning from related data fails to improve performance as the related
data can be inconsistent with the target task. We describe a meta-learning
based method that selectively learns from the related dialog task data. Our
approach leads to significant accuracy improvements in an example dialog task.

本文描述了一种基于元学习的方法，通过选择性地从相关的对话任务数据中学习，使用少量的数据以及来自相关对话任务的数据来训练神经对话系统，从而显著提高了对话任务的准确性。