# GraphFlow: Exploiting Conversation Flow with Graph Neural Networks for Conversational Machine Comprehension

arXiv: https://arxiv.org/abs/1908.00059 · HTML: https://mlsanity.com/paper/1908.00059/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2019.

## Built on by (most cited first, 30 of 41)

- [Retrieval-Augmented Generation for Code Summarization via Hybrid GNN](https://mlsanity.com/paper/2006.05405/) (2020)
- [Neural Approaches to Conversational Information Retrieval](https://mlsanity.com/paper/2201.05176/) (2022)
- [Conversational Question Answering: A Survey](https://mlsanity.com/paper/2106.00874/) (2021)
- [Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering](https://mlsanity.com/paper/2106.11575/) (2021)
- [Conversational Question Answering on Heterogeneous Sources](https://mlsanity.com/paper/2204.11677/) (2022)
- [Data-centric Graph Learning: A Survey](https://mlsanity.com/paper/2310.04987/) (2023)
- [ChainCQG: Flow-Aware Conversational Question Generation](https://mlsanity.com/paper/2102.02864/) (2021)
- [Ditch the Gold Standard: Re-evaluating Conversational Question Answering](https://mlsanity.com/paper/2112.08812/) (2021)
- [Saving Dense Retriever from Shortcut Dependency in Conversational Search](https://mlsanity.com/paper/2202.07280/) (2022)
- [Generating Information-Seeking Conversations from Unlabeled Documents](https://mlsanity.com/paper/2205.12609/) (2022)
- [RoR: Read-over-Read for Long Document Machine Reading Comprehension](https://mlsanity.com/paper/2109.04780/) (2021)
- [GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search](https://mlsanity.com/paper/2111.02671/) (2021)
- [Orders Are Unwanted: Dynamic Deep Graph Convolutional Network for Personality Detection](https://mlsanity.com/paper/2212.01515/) (2022)
- [BERT-CoQAC: BERT-based Conversational Question Answering in Context](https://mlsanity.com/paper/2104.11394/) (2021)
- [On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method](https://mlsanity.com/paper/2206.14796/) (2022)
- [PVG: Progressive Vision Graph for Vision Recognition](https://mlsanity.com/paper/2308.00574/) (2023)
- [QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance](https://mlsanity.com/paper/2204.13921/) (2022)
- [Open-Domain Conversational Question Answering with Historical Answers](https://mlsanity.com/paper/2211.09401/) (2022)
- [Reddit Entity Linking Dataset](https://mlsanity.com/paper/2101.01228/) (2021)
- [A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering](https://mlsanity.com/paper/2104.08443/) (2021)
- [Conversational Machine Reading Comprehension for Vietnamese Healthcare Texts](https://mlsanity.com/paper/2105.01542/) (2021)
- [Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients](https://mlsanity.com/paper/2407.04949/) (2024)
- [A Neural Question Answering System for Basic Questions about Subroutines](https://mlsanity.com/paper/2101.03999/) (2021)
- [Weakly-Supervised Open-Retrieval Conversational Question Answering](https://mlsanity.com/paper/2103.02537/) (2021)
- [History Semantic Graph Enhanced Conversational KBQA with Temporal Information Modeling](https://mlsanity.com/paper/2306.06872/) (2023)
- [Learning to Select the Relevant History Turns in Conversational Question Answering](https://mlsanity.com/paper/2308.02294/) (2023)
- [Feeding What You Need by Understanding What You Learned](https://mlsanity.com/paper/2203.02753/) (2022)
- [Robust Graph Structure Learning via Multiple Statistical Tests](https://mlsanity.com/paper/2210.03956/) (2022)
- [Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answering](https://mlsanity.com/paper/2304.07125/) (2023)
- [S2M: Converting Single-Turn to Multi-Turn Datasets for Conversational Question Answering](https://mlsanity.com/paper/2312.16511/) (2023)

## Abstract (arXiv)

Conversational machine comprehension (MC) has proven significantly more challenging compared to traditional MC since it requires better utilization of conversation history. However, most existing approaches do not effectively capture conversation history and thus have trouble handling questions involving coreference or ellipsis. Moreover, when reasoning over passage text, most of them simply treat it as a word sequence without exploring rich semantic relationships among words. In this paper, we first propose a simple yet effective graph structure learning technique to dynamically construct a question and conversation history aware context graph at each conversation turn. Then we propose a novel Recurrent Graph Neural Network, and based on that, we introduce a flow mechanism to model the temporal dependencies in a sequence of context graphs. The proposed GraphFlow model can effectively capture conversational flow in a dialog, and shows competitive performance compared to existing state-of-the-art methods on CoQA, QuAC and DoQA benchmarks. In addition, visualization experiments show that our proposed model can offer good interpretability for the reasoning process.