# Co-GAT: A Co-Interactive Graph Attention Network for Joint Dialog Act Recognition and Sentiment Classification

arXiv: https://arxiv.org/abs/2012.13260 · HTML: https://mlsanity.com/paper/2012.13260/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 29 of 29)

- [DynaEval: Unifying Turn and Dialogue Level Evaluation](https://mlsanity.com/paper/2106.01112/) (2021)
- [Control Globally, Understand Locally: A Global-to-Local Hierarchical Graph Network for Emotional Support Conversation](https://mlsanity.com/paper/2204.12749/) (2022)
- [Is ChatGPT Equipped with Emotional Dialogue Capabilities?](https://mlsanity.com/paper/2304.09582/) (2023)
- [Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization](https://mlsanity.com/paper/2105.12544/) (2021)
- [MAGDi: Structured Distillation of Multi-Agent Interaction Graphs Improves Reasoning in Smaller Language Models](https://mlsanity.com/paper/2402.01620/) (2024)
- [GL-GIN: Fast and Accurate Non-Autoregressive Model for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2106.01925/) (2021)
- [User Satisfaction Estimation with Sequential Dialogue Act Modeling in Goal-oriented Conversational Systems](https://mlsanity.com/paper/2202.02912/) (2022)
- [Speaker Turn Modeling for Dialogue Act Classification](https://mlsanity.com/paper/2109.05056/) (2021)
- [Modeling Multimodal Social Interactions: New Challenges and Baselines with Densely Aligned Representations](https://mlsanity.com/paper/2403.02090/) (2024)
- [DialMed: A Dataset for Dialogue-based Medication Recommendation](https://mlsanity.com/paper/2203.07094/) (2022)
- [Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling](https://mlsanity.com/paper/2210.10369/) (2022)
- [r-GAT: Relational Graph Attention Network for Multi-Relational Graphs](https://mlsanity.com/paper/2109.05922/) (2021)
- [DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition](https://mlsanity.com/paper/2203.03856/) (2022)
- [Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling](https://mlsanity.com/paper/2204.08152/) (2022)
- [Relational Temporal Graph Reasoning for Dual-task Dialogue Language Understanding](https://mlsanity.com/paper/2306.09114/) (2023)
- [Multi-Party Conversational Agents: A Survey](https://mlsanity.com/paper/2505.18845/) (2025)
- [Modeling User Satisfaction Dynamics in Dialogue via Hawkes Process](https://mlsanity.com/paper/2305.12594/) (2023)
- [CogIntAc: Modeling the Relationships between Intention, Emotion and Action in Interactive Process from Cognitive Perspective](https://mlsanity.com/paper/2205.03540/) (2022)
- [A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition](https://mlsanity.com/paper/2308.04424/) (2023)
- [Schema Graph-Guided Prompt for Multi-Domain Dialogue State Tracking](https://mlsanity.com/paper/2311.06345/) (2023)
- [A Speaker Turn-Aware Multi-Task Adversarial Network for Joint User Satisfaction Estimation and Sentiment Analysis](https://mlsanity.com/paper/2410.09556/) (2024)
- [Multi-Task Learning for Depression Detection in Dialogs](https://mlsanity.com/paper/2208.10250/) (2022)
- [Automatic Slide Updating with User-Defined Dynamic Templates and Natural Language Instructions](https://mlsanity.com/paper/2604.17894/) (2026)
- [Attention-based graph neural networks: a survey](https://mlsanity.com/paper/2605.08679/) (2026)
- [Requirements Elicitation in Cognitive Service for Recommendation](https://mlsanity.com/paper/2203.14958/) (2022)
- [Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension](https://mlsanity.com/paper/2210.14456/) (2022)
- [Speaker Profiling in Multiparty Conversations](https://mlsanity.com/paper/2304.08801/) (2023)
- [LaDA: Latent Dialogue Action For Zero-shot Cross-lingual Neural Network Language Modeling](https://mlsanity.com/paper/2308.02903/) (2023)
- [ReCoQA: A Benchmark for Tool-Augmented and Multi-Step Reasoning in Real Estate Question and Answering](https://mlsanity.com/paper/2604.17944/) (2026)

## Abstract (arXiv)

In a dialog system, dialog act recognition and sentiment classification are two correlative tasks to capture speakers intentions, where dialog act and sentiment can indicate the explicit and the implicit intentions separately. The dialog context information (contextual information) and the mutual interaction information are two key factors that contribute to the two related tasks. Unfortunately, none of the existing approaches consider the two important sources of information simultaneously. In this paper, we propose a Co-Interactive Graph Attention Network (Co-GAT) to jointly perform the two tasks. The core module is a proposed co-interactive graph interaction layer where a cross-utterances connection and a cross-tasks connection are constructed and iteratively updated with each other, achieving to consider the two types of information simultaneously. Experimental results on two public datasets show that our model successfully captures the two sources of information and achieve the state-of-the-art performance. In addition, we find that the contributions from the contextual and mutual interaction information do not fully overlap with contextualized word representations (BERT, Roberta, XLNet).