---
title: "A Hierarchical Latent Variable Encoder-Decoder — Serban 2016"
description: "Standard RNN-based dialogue generators, including RNNLM and HRED, introduce randomness only when each output token is sampled."
authors: ["Iulian Vlad Serban", "Alessandro Sordoni", "Ryan Lowe", "Laurent Charlin", "Joelle Pineau", "Aaron Courville", "Yoshua Bengio"]
year: 2016
venue: "arXiv:1605.06069"
source: "https://arxiv.org/abs/1605.06069"
doi: "10.48550/arxiv.1605.06069"
arxiv: "1605.06069"
licence: "arXiv — non-exclusive licence to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/serban-2016-hierarchical-latent-variable-encoder-decoder/"
---
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# A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

**Authors:** Iulian Vlad Serban, [Alessandro Sordoni](https://md.research.elamur.ai/free/en/authors/alessandro-sordoni/), [Ryan Lowe](https://md.research.elamur.ai/free/en/authors/ryan-lowe/), Laurent Charlin, [Joelle Pineau](https://md.research.elamur.ai/free/en/authors/joelle-pineau/), [Aaron Courville](https://md.research.elamur.ai/free/en/authors/aaron-courville/), [Yoshua Bengio](https://md.research.elamur.ai/free/en/authors/yoshua-bengio/) · **Year:** 2016 · **Venue:** arXiv:1605.06069 · **Licence:** [arXiv — non-exclusive licence to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/1605.06069) · [PDF](https://arxiv.org/pdf/1605.06069) · [DOI 10.48550/arxiv.1605.06069](https://doi.org/10.48550/arxiv.1605.06069) · [arXiv 1605.06069](https://arxiv.org/abs/1605.06069) · [OpenAlex](https://openalex.org/W2399880602)

**Citations:** 234 citations (openalex-snapshot · 2026-10-14) · [284](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Standard RNN-based dialogue generators, including RNNLM and HRED, introduce randomness only when each output token is sampled. The authors argue that this pushes models toward local, next-token prediction and makes them lose the broader conversational thread, which often yields bland, generic replies. They propose VHRED, which extends HRED's encoder, context and decoder RNNs with a continuous Gaussian latent variable sampled once per utterance. Both the prior and the approximate posterior over this variable are produced by feed-forward networks on top of the context RNN, and training maximizes a variational lower bound in the style of the VAE. The latent variable is meant to capture high-level choices such as topic or sentiment, so the decoder's hidden state can concentrate on word-level detail. The model is tested on response generation for Twitter and Ubuntu dialogues against LSTM and HRED baselines, using a human study on Amazon Mechanical Turk, embedding-based similarity metrics and entropy analysis. VHRED stays closer to the topic of the context, especially across three generated turns, and its responses carry more information. The design applies to any hierarchically structured sequence generation task.

## Key points
- **Architecture:** VHRED adds one diagonal-Gaussian latent variable per utterance to HRED. It is drawn from the approximate posterior during training and from the conditional prior at test time, then concatenated with the context RNN output before decoding. The authors retained a direct deterministic connection feeding the context RNN's output straight into the decoder, since doing so reduced the variational bound.
- **Evaluation setup:** Two dialogue corpora, Twitter and Ubuntu, were used, with LSTM and HRED as baselines. Evaluation combined human judgments on Amazon Mechanical Turk with word2vec embedding-based metrics for 1-turn and 3-turn generation.
- **Topical coherence:** VHRED matched the topic of the ground-truth response better than LSTM and HRED, with the clearest gains when generating three consecutive utterances.
- **Information content on Twitter (Table 4):** VHRED produced the longest responses (12.29 tokens vs. 11.64 for HRED and 11.21 for LSTM). It also had the highest utterance entropy (84.56 bits vs. 78.35 and 75.61). Human responses remain far richer, at 166.57 bits.
- **Information content on Ubuntu (Table 4):** VHRED had the highest per-word entropy (7.70 vs. 7.53 for HRED and 6.50 for LSTM). However, its responses were shorter than HRED's (9.22 vs. 11.05 tokens), so its utterance entropy was lower (71.00 vs. 83.16).

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

Fields: [Other](https://md.research.elamur.ai/free/en/shelves/other/)

## Cited by

Cited by

284 papers of our library cite it (of the 140673 whose bibliographies we read); 14 of them have a page here, 270 not yet.

- [Dialogue Act Sequence Labeling using Hierarchical encoder with CRF](https://md.research.elamur.ai/free/en/papers/kumar-2017-dialogue-act-sequence-labeling-using/)
- [Adversarial Learning for Neural Dialogue Generation](https://md.research.elamur.ai/free/en/papers/li-2017-adversarial-learning-neural-dialogue-generation/)
- [DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset](https://md.research.elamur.ai/free/en/papers/li-2017-dailydialog-manually-labelled-multi-turn/)
- [Towards an Automatic Turing Test: Learning to Evaluate Dialogue Responses](https://md.research.elamur.ai/free/en/papers/lowe-2017-towards-automatic-turing-test-learning/)
- [Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems](https://md.research.elamur.ai/free/en/papers/madotto-2018-mem2seq-effectively-incorporating-knowledge-bases/)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://md.research.elamur.ai/free/en/papers/majumder-2020-mime-mimicking-emotions-empathetic-response/)
- [Towards Exploiting Background Knowledge for Building Conversation Systems](https://md.research.elamur.ai/free/en/papers/moghe-2018-exploiting-background-knowledge-building-conversation/)
- [Scaling Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/ott-2018-scaling-neural-machine-translation/)
- [MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations](https://md.research.elamur.ai/free/en/papers/poria-2018-meld-multimodal-multi-party-dataset/)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://md.research.elamur.ai/free/en/papers/zhang-2018-personalizing-dialogue-agents-i-have/)
- [DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation](https://md.research.elamur.ai/free/en/papers/zhang-2019-dialogpt-large-scale-generative-pre/)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://md.research.elamur.ai/free/en/papers/zhao-2017-learning-discourse-level-diversity-neural/)
- [Knowledge-Grounded Dialogue Generation with Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2020-knowledge-grounded-dialogue-generation-pre/)
- [Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality](https://md.research.elamur.ai/free/en/papers/zhou-2022-reflect-not-reflex-inference-based/)

## Cite

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

```bibtex
@misc{serban2016hierarchical,
  title = {{A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues}},
  author = {Serban, Iulian Vlad and Sordoni, Alessandro and Lowe, Ryan and Charlin, Laurent and Pineau, Joelle and Courville, Aaron and Bengio, Yoshua},
  year = {2016},
  doi = {10.48550/arxiv.1605.06069},
  eprint = {1605.06069},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.1605.06069}
}
```

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