---
title: "Modeling Multi-turn Conversation with Deep Utterance Aggregation"
description: "The authors propose Deep Utterance Aggregation (DUA), which has five modules: GRU encoding of… 45 references, cited by 81 papers here."
authors: ["Zhuosheng Zhang", "Jiangtong Li", "Pengfei Zhu", "Hai Zhao", "Gongshen Liu"]
year: 2018
venue: "Proceedings of the 27th International Conference on Computational Linguistics"
source: "https://aclanthology.org/C18-1317/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zhang-2018-modeling-multi-turn-conversation-deep/"
---
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# Modeling Multi-turn Conversation with Deep Utterance Aggregation

**Authors:** [Zhuosheng Zhang](https://md.research.elamur.ai/free/en/authors/zhuosheng-zhang/), Jiangtong Li, Pengfei Zhu, [Hai Zhao](https://md.research.elamur.ai/free/en/authors/hai-zhao/) and [Gongshen Liu](https://md.research.elamur.ai/free/en/authors/gongshen-liu/) · **Year:** 2018 · **Venue:** [Proceedings of the 27th International Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/coling/) ([COLING 2018](https://md.research.elamur.ai/free/en/venues/coling/2018/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/C18-1317/) · [PDF](https://aclanthology.org/C18-1317.pdf) · [OpenAlex](https://openalex.org/W2822830299)

**Citations:** 71 citations (OpenAlex, 2026-10-14) · [81](https://md.research.elamur.ai/free/en/papers/zhang-2018-modeling-multi-turn-conversation-deep/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper tackles retrieval-based response selection for multi-turn dialogue. Earlier systems either concatenate the whole history, which adds noise, or match the candidate against each turn without weighing which turns and words matter. The authors propose Deep Utterance Aggregation (DUA), which has five modules:
- GRU encoding of utterances and responses.
- Turns-aware fusion that concatenates every turn and the candidate with the latest user message.
- Gated self-matching attention that keeps salient words and suppresses redundancy.
- Word- and utterance-level response matching fed through a CNN.
- A final GRU that aggregates the per-turn matching vectors in chronological order to produce a score.

The authors also release the E-commerce Dialogue Corpus (ECD), which they describe as the first public e-commerce dialogue dataset built from real human conversations. It covers consultation, logistics, recommendation, negotiation and chitchat. DUA reports state-of-the-art results on three benchmarks, including ECD. This matters because it shows that explicitly modeling the importance of turns and words improves context understanding. The new corpus also gives a harder testbed with many intents and topics.

## Key points
- On ECD, DUA reaches R10@1 0.501, R10@2 0.700 and R10@5 0.921.
- In the ablation, dropping Matching Attention Flow costs the most (R10@1 falls to 0.432, −6.9 points). Removing Context Fusion lowers it to 0.453 (−4.8). Removing both gives 0.413.
- ECD conversation-type shares include Consultation 36.1%, Chitchat 26.3%, Logistics 7.3%, Negotiation 5.9% and Recommendation 4.4%.
- Recommendation, consultation and negotiation are harder than chitchat and logistics. Negotiation is the hardest, with R10@1 0.385.
- Error analysis points to three main failure types: messages carrying several intents, confusion between commodities (topic errors), and valid paraphrased responses counted as wrong under single-gold evaluation.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Shanghai Jiao Tong University](https://md.research.elamur.ai/free/en/universities/shanghai-jiao-tong-university/) — University, Shanghai, China · 63 works · 15,047 citations
- [Chinese Academy of Sciences](https://md.research.elamur.ai/free/en/government/chinese-academy-of-sciences/) — Government body, Beijing, China · 68 works · 22,753 citations
- [East China Normal University](https://md.research.elamur.ai/free/en/universities/east-china-normal-university/) — University, Shanghai, China · 19 works · 1,856 citations

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## Cite

Cite as: Zhuosheng Zhang, Jiangtong Li, Pengfei Zhu, Hai Zhao and Gongshen Liu. 2018. Modeling Multi-turn Conversation with Deep Utterance Aggregation. Proceedings of the 27th International Conference on Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zhang2018modeling,
  title = {{Modeling Multi-turn Conversation with Deep Utterance Aggregation}},
  author = {Zhuosheng Zhang, Jiangtong Li, Pengfei Zhu, Hai Zhao and Gongshen Liu},
  booktitle = {Proceedings of the 27th International Conference on Computational Linguistics},
  year = {2018},
  url = {https://aclanthology.org/C18-1317/}
}
```

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*Updated 24 October 2026*