# Multi-turn Dialog System on Single-turn Data in Medical Domain

arXiv: https://arxiv.org/abs/2105.12887 · HTML: https://mlsanity.com/paper/2105.12887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 14 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 7 of 7)

- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [IQA: Visual Question Answering in Interactive Environments](https://mlsanity.com/paper/1712.03316/) (2017)
- [Towards Knowledge-Based Recommender Dialog System](https://mlsanity.com/paper/1908.05391/) (2019)
- [Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems](https://mlsanity.com/paper/1804.08217/) (2018)
- [Modeling Multi-turn Conversation with Deep Utterance Aggregation](https://mlsanity.com/paper/1806.09102/) (2018)
- [A Context-aware Attention Network for Interactive Question Answering](https://mlsanity.com/paper/1612.07411/) (2016)

## Built on by (most cited first, 1 of 1)

- [Agent-Based Detection and Resolution of Incompleteness and Ambiguity in Interactions with Large Language Models](https://mlsanity.com/paper/2507.03726/) (2025)

## Abstract (arXiv)

Recently there has been a huge interest in dialog systems. This interest has also been developed in the field of the medical domain where researchers are focusing on building a dialog system in the medical domain. This research is focused on the multi-turn dialog system trained on the multi-turn dialog data. It is difficult to gather a huge amount of multi-turn conversational data in the medical domain that is verified by professionals and can be trusted. However, there are several frequently asked questions (FAQs) or single-turn QA pairs that have information that is verified by the experts and can be used to build a multi-turn dialog system.