# Modeling Multi-turn Conversation with Deep Utterance Aggregation

arXiv: https://arxiv.org/abs/1806.09102 · HTML: https://mlsanity.com/paper/1806.09102/

## Key facts

- Cited by 65 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2018.

## Built on by (most cited first, 30 of 65)

- [Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey](https://mlsanity.com/paper/2105.04387/) (2021)
- [MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation](https://mlsanity.com/paper/2211.05719/) (2022)
- [CoMPM: Context Modeling with Speaker's Pre-trained Memory Tracking for Emotion Recognition in Conversation](https://mlsanity.com/paper/2108.11626/) (2021)
- [On the Calibration and Uncertainty of Neural Learning to Rank Models](https://mlsanity.com/paper/2101.04356/) (2021)
- [Structural Pre-training for Dialogue Comprehension](https://mlsanity.com/paper/2105.10956/) (2021)
- [DialogueCSE: Dialogue-based Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2109.12599/) (2021)
- [Crossing the Conversational Chasm: A Primer on Natural Language Processing for Multilingual Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.08570/) (2021)
- [Partner Matters! An Empirical Study on Fusing Personas for Personalized Response Selection in Retrieval-Based Chatbots](https://mlsanity.com/paper/2105.09050/) (2021)
- [Robust Dialogue State Tracking with Weak Supervision and Sparse Data](https://mlsanity.com/paper/2202.03354/) (2022)
- [Do Large Language Models Understand Conversational Implicature -- A case study with a chinese sitcom](https://mlsanity.com/paper/2404.19509/) (2024)
- [Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots](https://mlsanity.com/paper/2103.09534/) (2021)
- [Automatic Intent-Slot Induction for Dialogue Systems](https://mlsanity.com/paper/2103.08886/) (2021)
- [Self- and Pseudo-self-supervised Prediction of Speaker and Key-utterance for Multi-party Dialogue Reading Comprehension](https://mlsanity.com/paper/2109.03772/) (2021)
- [Enhanced Speaker-aware Multi-party Multi-turn Dialogue Comprehension](https://mlsanity.com/paper/2109.04066/) (2021)
- [Recall and Learn: A Memory-augmented Solver for Math Word Problems](https://mlsanity.com/paper/2109.13112/) (2021)
- [RE-Matching: A Fine-Grained Semantic Matching Method for Zero-Shot Relation Extraction](https://mlsanity.com/paper/2306.04954/) (2023)
- [Advances in Multi-turn Dialogue Comprehension: A Survey](https://mlsanity.com/paper/2103.03125/) (2021)
- [K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce](https://mlsanity.com/paper/2104.06960/) (2021)
- [Learning Implicit User Profiles for Personalized Retrieval-Based Chatbot](https://mlsanity.com/paper/2108.07935/) (2021)
- [Exploring Dense Retrieval for Dialogue Response Selection](https://mlsanity.com/paper/2110.06612/) (2021)
- [Multi-turn Dialogue Reading Comprehension with Pivot Turns and Knowledge](https://mlsanity.com/paper/2102.05474/) (2021)
- [Overview of Robust and Multilingual Automatic Evaluation Metrics for Open-Domain Dialogue Systems at DSTC 11 Track 4](https://mlsanity.com/paper/2306.12794/) (2023)
- [Dialogue-oriented Pre-training](https://mlsanity.com/paper/2106.00420/) (2021)
- [Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations](https://mlsanity.com/paper/2109.13087/) (2021)
- [CGoDial: A Large-Scale Benchmark for Chinese Goal-oriented Dialog Evaluation](https://mlsanity.com/paper/2211.11617/) (2022)
- [You Truly Understand What I Need: Intellectual and Friendly Dialogue Agents grounding Knowledge and Persona](https://mlsanity.com/paper/2301.02401/) (2023)
- [P5: Plug-and-Play Persona Prompting for Personalized Response Selection](https://mlsanity.com/paper/2310.06390/) (2023)
- [Structural Characterization for Dialogue Disentanglement](https://mlsanity.com/paper/2110.08018/) (2021)
- [Back to the Future: Bidirectional Information Decoupling Network for Multi-turn Dialogue Modeling](https://mlsanity.com/paper/2204.08152/) (2022)
- [Smoothing Dialogue States for Open Conversational Machine Reading](https://mlsanity.com/paper/2108.12599/) (2021)

## Abstract (arXiv)

Multi-turn conversation understanding is a major challenge for building intelligent dialogue systems. This work focuses on retrieval-based response matching for multi-turn conversation whose related work simply concatenates the conversation utterances, ignoring the interactions among previous utterances for context modeling. In this paper, we formulate previous utterances into context using a proposed deep utterance aggregation model to form a fine-grained context representation. In detail, a self-matching attention is first introduced to route the vital information in each utterance. Then the model matches a response with each refined utterance and the final matching score is obtained after attentive turns aggregation. Experimental results show our model outperforms the state-of-the-art methods on three multi-turn conversation benchmarks, including a newly introduced e-commerce dialogue corpus.