# Improving Medical Dialogue Generation with Abstract Meaning Representations

arXiv: https://arxiv.org/abs/2309.10608 · HTML: https://mlsanity.com/paper/2309.10608/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 24 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://mlsanity.com/paper/1911.00536/) (2019)
- [MedDialog: Two Large-scale Medical Dialogue Datasets](https://mlsanity.com/paper/2004.03329/) (2020)
- [Neural AMR: Sequence-to-Sequence Models for Parsing and Generation](https://mlsanity.com/paper/1704.08381/) (2017)
- [AMR Parsing via Graph-Sequence Iterative Inference](https://mlsanity.com/paper/2004.05572/) (2020)
- [Modeling Graph Structure in Transformer for Better AMR-to-Text Generation](https://mlsanity.com/paper/1909.00136/) (2019)
- [Semantic Representation for Dialogue Modeling](https://mlsanity.com/paper/2105.10188/) (2021)
- [EtriCA: Event-Triggered Context-Aware Story Generation Augmented by Cross Attention](https://mlsanity.com/paper/2210.12463/) (2022)
- [Enhancing Dialogue Generation via Dynamic Graph Knowledge Aggregation](https://mlsanity.com/paper/2306.16195/) (2023)
- [Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition](https://mlsanity.com/paper/2004.10663/) (2020)
- [Terminology-aware Medical Dialogue Generation](https://mlsanity.com/paper/2210.15551/) (2022)
- [Evaluating Open-Domain Dialogues in Latent Space with Next Sentence Prediction and Mutual Information](https://mlsanity.com/paper/2305.16967/) (2023)

## Built on by (most cited first, 10 of 10)

- [Effective Distillation of Table-based Reasoning Ability from LLMs](https://mlsanity.com/paper/2309.13182/) (2023)
- [SLIDE: A Framework Integrating Small and Large Language Models for Open-Domain Dialogues Evaluation](https://mlsanity.com/paper/2405.15924/) (2024)
- [Improving Biomedical Abstractive Summarisation with Knowledge Aggregation from Citation Papers](https://mlsanity.com/paper/2310.15684/) (2023)
- [A Cross-Attention Augmented Model for Event-Triggered Context-Aware Story Generation](https://mlsanity.com/paper/2311.11271/) (2023)
- [Train & Constrain: Phonologically Informed Tongue-Twister Generation from Topics and Paraphrases](https://mlsanity.com/paper/2403.13901/) (2024)
- [Emphasising Structured Information: Integrating Abstract Meaning Representation into LLMs for Enhanced Open-Domain Dialogue Evaluation](https://mlsanity.com/paper/2404.01129/) (2024)
- [DRE: An Effective Dual-Refined Method for Integrating Small and Large Language Models in Open-Domain Dialogue Evaluation](https://mlsanity.com/paper/2506.04516/) (2025)
- [ACL Anthology Helper: A Tool to Retrieve and Manage Literature from ACL Anthology](https://mlsanity.com/paper/2310.20467/) (2023)
- [BioMNER: A Dataset for Biomedical Method Entity Recognition](https://mlsanity.com/paper/2406.20038/) (2024)
- [Impact of enriched meaning representations for language generation in dialogue tasks: A comprehensive exploration of the relevance of tasks, corpora and metrics](https://mlsanity.com/paper/2603.29518/) (2026)

## Abstract (arXiv)

Medical Dialogue Generation serves a critical role in telemedicine by facilitating the dissemination of medical expertise to patients. Existing studies focus on incorporating textual representations, which have limited their ability to represent the semantics of text, such as ignoring important medical entities. To enhance the model's understanding of the textual semantics and the medical knowledge including entities and relations, we introduce the use of Abstract Meaning Representations (AMR) to construct graphical representations that delineate the roles of language constituents and medical entities within the dialogues. In this paper, We propose a novel framework that models dialogues between patients and healthcare professionals using AMR graphs, where the neural networks incorporate textual and graphical knowledge with a dual attention mechanism. Experimental results show that our framework outperforms strong baseline models in medical dialogue generation, demonstrating the effectiveness of AMR graphs in enhancing the representations of medical knowledge and logical relationships. Furthermore, to support future research in this domain, we provide the corresponding source code at https://github.com/Bernard-Yang/MedDiaAMR.