# Dialogue Graph Modeling for Conversational Machine Reading

arXiv: https://arxiv.org/abs/2012.14827 · HTML: https://mlsanity.com/paper/2012.14827/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2020.

## Built on by (most cited first, 26 of 26)

- [Pretrained Language Models for Text Generation: A Survey](https://mlsanity.com/paper/2201.05273/) (2022)
- [The Shifted and The Overlooked: A Task-oriented Investigation of User-GPT Interactions](https://mlsanity.com/paper/2310.12418/) (2023)
- [MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models](https://mlsanity.com/paper/2402.01620/) (2024)
- [Social Influence Dialogue Systems: A Survey of Datasets and Models For Social Influence Tasks](https://mlsanity.com/paper/2210.05664/) (2022)
- [Controlling Personality Style in Dialogue with Zero-Shot Prompt-Based Learning](https://mlsanity.com/paper/2302.03848/) (2023)
- [Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension](https://mlsanity.com/paper/2109.04066/) (2021)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2103.03125/) (2021)
- [Dialogue-oriented Pre-training](https://mlsanity.com/paper/2106.00420/) (2021)
- [Ontologically Faithful Generation of Non-Player Character Dialogues](https://mlsanity.com/paper/2212.10618/) (2022)
- [Smoothing Dialogue States for Open Conversational Machine Reading](https://mlsanity.com/paper/2108.12599/) (2021)
- [Multi-tasking Dialogue Comprehension with Discourse Parsing](https://mlsanity.com/paper/2110.03269/) (2021)
- [Information Re-Organization Improves Reasoning in Large Language Models](https://mlsanity.com/paper/2404.13985/) (2024)
- [Structured Dialogue Discourse Parsing](https://mlsanity.com/paper/2306.15103/) (2023)
- [Reasoning over Logically Interacted Conditions for Question Answering](https://mlsanity.com/paper/2205.12898/) (2022)
- [ET5: A Novel End-to-end Framework for Conversational Machine Reading Comprehension](https://mlsanity.com/paper/2209.11484/) (2022)
- [Towards End-to-End Open Conversational Machine Reading](https://mlsanity.com/paper/2210.07113/) (2022)
- [Revisiting Conversation Discourse for Dialogue Disentanglement](https://mlsanity.com/paper/2306.03975/) (2023)
- [PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs](https://mlsanity.com/paper/2402.12835/) (2024)
- [Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension](https://mlsanity.com/paper/2306.12069/) (2023)
- [Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems](https://mlsanity.com/paper/2307.07255/) (2023)
- [Explicit Alignment and Many-to-many Entailment Based Reasoning for Conversational Machine Reading](https://mlsanity.com/paper/2310.13409/) (2023)
- [Unsupervised Technique To Conversational Machine Reading](https://mlsanity.com/paper/2106.15247/) (2021)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2110.04984/) (2021)
- [Tracing Origins: Coreference-aware Machine Reading Comprehension](https://mlsanity.com/paper/2110.07961/) (2021)
- [Bridging The Gap: Entailment Fused-T5 for Open-retrieval Conversational Machine Reading Comprehension](https://mlsanity.com/paper/2212.09353/) (2022)
- [STRUDEL: Structured Dialogue Summarization for Dialogue Comprehension](https://mlsanity.com/paper/2212.12652/) (2022)

## Abstract (arXiv)

Conversational Machine Reading (CMR) aims at answering questions in a complicated manner. Machine needs to answer questions through interactions with users based on given rule document, user scenario and dialogue history, and ask questions to clarify if necessary. In this paper, we propose a dialogue graph modeling framework to improve the understanding and reasoning ability of machine on CMR task. There are three types of graph in total. Specifically, Discourse Graph is designed to learn explicitly and extract the discourse relation among rule texts as well as the extra knowledge of scenario; Decoupling Graph is used for understanding local and contextualized connection within rule texts. And finally a global graph for fusing the information together and reply to the user with our final decision being either "Yes/No/Irrelevant" or to ask a follow-up question to clarify.