---
title: "A Network-based End-to-End Trainable Task-oriented Dialogue"
description: "The authors propose a neural network-based system that takes text as input and returns text. 38 references, cited by 388 papers here."
authors: ["Tsung-Hsien Wen", "David Vandyke", "Nikola Mrkšić", "Milica Gašić", "Lina M. Rojas-Barahona", "Pei-Hao Su", "Stefan Ultes", "Steve Young"]
year: 2017
venue: "Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers"
source: "https://aclanthology.org/E17-1042/"
arxiv: "1604.04562"
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/wen-2017-network-based-end-end-trainable/"
---
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# A Network-based End-to-End Trainable Task-oriented Dialogue System

**Authors:** [Tsung-Hsien Wen](https://md.research.elamur.ai/free/en/authors/tsung-hsien-wen/), [David Vandyke](https://md.research.elamur.ai/free/en/authors/david-vandyke/), [Nikola Mrkšić](https://md.research.elamur.ai/free/en/authors/nikola-mrksic/), [Milica Gašić](https://md.research.elamur.ai/free/en/authors/milica-gasic/), [Lina M. Rojas-Barahona](https://md.research.elamur.ai/free/en/authors/lina-m-rojas-barahona/), [Pei-Hao Su](https://md.research.elamur.ai/free/en/authors/pei-hao-su/), [Stefan Ultes](https://md.research.elamur.ai/free/en/authors/stefan-ultes/) and [Steve Young](https://md.research.elamur.ai/free/en/authors/steve-young/) · **Year:** 2017 · **Venue:** [Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2017](https://md.research.elamur.ai/free/en/venues/eacl/2017/)) · **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/E17-1042/) · [PDF](https://aclanthology.org/E17-1042.pdf) · [arXiv 1604.04562](https://arxiv.org/abs/1604.04562) · [OpenAlex](https://openalex.org/W2340944142)

**Citations:** 146 citations (OpenAlex, 2026-10-14) · [388](https://md.research.elamur.ai/free/en/papers/wen-2017-network-based-end-end-trainable/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary

The paper addresses the construction of dialogue systems that help a user accomplish a specific task, such as finding a restaurant. Such systems usually consist of many separate modules, each of which requires either manual design or costly annotation. The authors propose a neural network-based system that takes text as input and returns text. It can be trained end-to-end, yet it retains a modular structure. An intent network based on an LSTM or CNN encodes the user's utterance. Belief trackers estimate a probability distribution over slot–value pairs. Based on these estimates, the system queries the database. A policy network combines all these signals into a single action vector. From this vector, the generator produces a delexicalised response, into which actual values from the database are then substituted. The training data were collected via crowdsourcing using a novel pipelined Wizard-of-Oz scheme that directly yields annotations for the trackers. Training on a few hundred dialogues in the restaurant search domain produced a system that human judges rated higher than a strong modular baseline with handcrafted rules. The work shows that end-to-end training is practical for task-oriented dialogue even with a small amount of data.

## Key points

- **Architecture:** an intent network, belief trackers (a Jordan-type RNN with a CNN feature extractor and shared weights), a DB operator, a policy network and an LSTM generator. Delexicalisation replaces values with generic tokens, such as `<v.food>`, and thus reduces the need for data.
- **Attention and weighted decoding:** an attention mechanism over the tracker states increases task success by approximately 5%, while BLEU rises by only about 0.01. Weighted decoding yields a further improvement.
- **Human evaluation:** on Amazon Mechanical Turk, the full model was tested in 245 dialogues. Subjective success was 98%, comprehension 4.11 out of 5, and naturalness 4.05 out of 5.
- **Comparison with HDC (164 comparisons):** success of 96.95% versus 95.12%, with an average of 3.95 turns versus 4.54. The neural system was preferred by 50% of the judges, HDC by 24.39% (p \< 0.005). It also won on naturalness and comprehension.
- **Limitations:** the system works with text only, does not handle noisy speech recognition output, and does not ask the user for clarification when it is uncertain. Whether the approach scales to broader domains remains an open question.

*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

- [University of Cambridge](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) — University, Cambridge, United Kingdom · 243 works · 121,396 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

🧭 Topics (1): [🗨️ Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · 624 works

💡 Concepts (1): [💡 Chatbots](https://md.research.elamur.ai/free/en/concepts/chatbot/) · 54 works

## Cite

Cite as: Tsung-Hsien Wen, David Vandyke, Nikola Mrkšić, Milica Gašić, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes and Steve Young. 2017. A Network-based End-to-End Trainable Task-oriented Dialogue System. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers.

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

```bibtex
@inproceedings{wen2017network,
  title = {{A Network-based End-to-End Trainable Task-oriented Dialogue System}},
  author = {Tsung-Hsien Wen, David Vandyke, Nikola Mrkšić, Milica Gašić, Lina M. Rojas-Barahona, Pei-Hao Su, Stefan Ultes and Steve Young},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  year = {2017},
  eprint = {1604.04562},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/1604.04562}
}
```

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*Updated 29 October 2026*