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

TL;DR

This paper introduces CDialog, a new multi-turn Covid-19 medical dialog dataset with annotated medical entities, and proposes a neural dialog system that leverages entity information to improve response quality.

Contribution

First to release a high-quality multi-turn Covid-19 medical dialog dataset with detailed entity annotations and develop a neural dialog model that effectively incorporates these entities.

Findings

Entity-aware models outperform baseline in response quality

Annotated dataset facilitates training of medical dialog systems

Proposed system demonstrates improved response relevance

Abstract

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.