# MEDCOD: A Medically-Accurate, Emotive, Diverse, and Controllable Dialog System

arXiv: https://arxiv.org/abs/2111.09381 · HTML: https://mlsanity.com/paper/2111.09381/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 64 references that are in the map.
- First posted on arXiv 17 Nov 2021.

## Builds on (most cited first, 21 of 21)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [What Makes Good In-Context Examples for GPT-3?](https://mlsanity.com/paper/2101.06804/) (2021)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [A Simple Language Model for Task-Oriented Dialogue](https://mlsanity.com/paper/2005.00796/) (2020)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [A Neural Conversational Model](https://mlsanity.com/paper/1506.05869/) (2015)
- [TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue](https://mlsanity.com/paper/2004.06871/) (2020)
- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [Clinically Accurate Chest X-Ray Report Generation](https://mlsanity.com/paper/1904.02633/) (2019)
- [Medically Aware GPT-3 as a Data Generator for Medical Dialogue Summarization](https://mlsanity.com/paper/2110.07356/) (2021)
- [Few-Shot NLG with Pre-Trained Language Model](https://mlsanity.com/paper/1904.09521/) (2019)
- [Neural Generation Meets Real People: Towards Emotionally Engaging Mixed-Initiative Conversations](https://mlsanity.com/paper/2008.12348/) (2020)
- [Graph-Evolving Meta-Learning for Low-Resource Medical Dialogue Generation](https://mlsanity.com/paper/2012.11988/) (2020)
- [Extracting Symptoms and their Status from Clinical Conversations](https://mlsanity.com/paper/1906.02239/) (2019)
- [Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction](https://mlsanity.com/paper/2104.02219/) (2021)
- [Learning to Infer Entities, Properties and their Relations from Clinical Conversations](https://mlsanity.com/paper/1908.11536/) (2019)
- [BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems](https://mlsanity.com/paper/1608.05081/) (2016)
- [Learning from the experts: From expert systems to machine-learned diagnosis models](https://mlsanity.com/paper/1804.08033/) (2018)
- [COVID-19 in differential diagnosis of online symptom assessments](https://mlsanity.com/paper/2008.03323/) (2020)

## Built on by (most cited first, 3 of 3)

- [Injecting knowledge into language generation: a case study in auto-charting after-visit care instructions from medical dialogue](https://mlsanity.com/paper/2306.03652/) (2023)
- [Mind the Knowledge Gap: A Survey of Knowledge-enhanced Dialogue Systems](https://mlsanity.com/paper/2212.09252/) (2022)
- [Dialogue-Contextualized Re-ranking for Medical History-Taking](https://mlsanity.com/paper/2304.01974/) (2023)

## Abstract (arXiv)

We present MEDCOD, a Medically-Accurate, Emotive, Diverse, and Controllable Dialog system with a unique approach to the natural language generator module. MEDCOD has been developed and evaluated specifically for the history taking task. It integrates the advantage of a traditional modular approach to incorporate (medical) domain knowledge with modern deep learning techniques to generate flexible, human-like natural language expressions. Two key aspects of MEDCOD's natural language output are described in detail. First, the generated sentences are emotive and empathetic, similar to how a doctor would communicate to the patient. Second, the generated sentence structures and phrasings are varied and diverse while maintaining medical consistency with the desired medical concept (provided by the dialogue manager module of MEDCOD). Experimental results demonstrate the effectiveness of our approach in creating a human-like medical dialogue system. Relevant code is available at https://github.com/curai/curai-research/tree/main/MEDCOD