---
title: "Personalizing Dialogue Agents — Zhang 2018"
description: "Open-domain chit-chat bots often feel bland. Their personality shifts from turn to turn, they forget earlier context, and they fall back on vague…"
authors: ["Saizheng Zhang", "Emily Dinan", "Jack Urbanek", "Arthur Szlam", "Douwe Kiela", "Jason Weston"]
year: 2018
venue: "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/P18-1205/"
doi: "10.18653/v1/p18-1205"
arxiv: "1801.07243"
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-personalizing-dialogue-agents-i-have/"
---
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# Personalizing Dialogue Agents: I have a dog, do you have pets too?

**Authors:** [Saizheng Zhang](https://md.research.elamur.ai/free/en/authors/saizheng-zhang/), [Emily Dinan](https://md.research.elamur.ai/free/en/authors/emily-dinan/), [Jack Urbanek](https://md.research.elamur.ai/free/en/authors/jack-urbanek/), [Arthur Szlam](https://md.research.elamur.ai/free/en/authors/arthur-szlam/), [Douwe Kiela](https://md.research.elamur.ai/free/en/authors/douwe-kiela/), [Jason Weston](https://md.research.elamur.ai/free/en/authors/jason-weston/) · **Year:** 2018 · **Venue:** Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/P18-1205/) · [PDF](https://aclanthology.org/P18-1205.pdf) · [DOI 10.18653/v1/p18-1205](https://doi.org/10.18653/v1/p18-1205) · [arXiv 1801.07243](https://arxiv.org/abs/1801.07243) · [OpenAlex](https://openalex.org/W2963825865)

**Citations:** 1153 citations (openalex-snapshot · 2026-10-14) · [686](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Open-domain chit-chat bots often feel bland. Their personality shifts from turn to turn, they forget earlier context, and they fall back on vague replies. The authors address this by giving a dialogue agent an explicit text persona, a few short sentences describing who it is. They also release PERSONA-CHAT, a crowdsourced English corpus built around such profiles. Pairs of Mechanical Turk workers each got a random persona and chatted to get to know each other. A separate group of workers then rewrote every persona in new wording, so models cannot succeed through simple word overlap. For next-utterance prediction, the paper compares IR baselines, Seq2Seq generators and ranking models, including Memory Networks and a Key-Value Profile Memory network. Giving a model its own persona improves prediction in both the generative and the ranking setting. Human judges found persona-conditioned agents more consistent but not more engaging. Every model trained on PERSONA-CHAT was rated above models trained on OpenSubtitles or Twitter. This makes the corpus a key benchmark for consistent, persona-grounded chatbots.

## Key points
- PERSONA-CHAT has 1,155 personas of at least 5 sentences each, with 100 held out for validation and 100 for test. It contains 10,981 dialogues and 164,356 utterances, and is released with code and models in ParlAI.
- Each persona also has a revised version: crowdworkers rewrote it as rephrasings, generalizations or specializations. This makes the task harder and gives a semantic-similarity resource.
- Conditioning on the agent's own persona helps next-utterance prediction. Conditioning on the partner's persona has much less effect, since speakers mostly talk about themselves.
- In human evaluation, workers chatted with models without knowing it and rated fluency, engagingness and consistency on a 1–5 scale. PERSONA-CHAT models beat models trained on OpenSubtitles (2009 and 2018 versions) and Twitter.
- In profile prediction, a speaker's persona could be identified from what they said with 94.3% accuracy. It was 23% when using only the KV Profile Memory model's utterances, and accuracy rose as dialogues got longer.

*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: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/)

## Cited by

Cited by

686 papers of our library cite it (of the 140673 whose bibliographies we read); 52 of them have a page here, 634 not yet.

- [Towards a Human-like Open-Domain Chatbot](https://md.research.elamur.ai/free/en/papers/adiwardana-2020-towards-human-like-open-domain/)
- [Building Common Ground in Dialogue: A Survey](https://md.research.elamur.ai/free/en/papers/anikina-2025-building-common-ground-dialogue-survey/)
- [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://md.research.elamur.ai/free/en/papers/chen-2021-dialogsum-real-life-scenario-dialogue/)
- [Automatic Evaluate Dialogue Appropriateness by Using Dialogue Act](https://md.research.elamur.ai/free/en/papers/chen-2023-automatic-evaluate-dialogue-appropriateness-dialogue/)
- [From Persona to Personalization: A Survey on Role-Playing Language Agents](https://md.research.elamur.ai/free/en/papers/chen-2024-persona-personalization-survey-role-playing/)
- [MuTual: A Dataset for Multi-Turn Dialogue Reasoning](https://md.research.elamur.ai/free/en/papers/cui-2020-mutual-dataset-multi-turn-dialogue/)
- [Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack](https://md.research.elamur.ai/free/en/papers/dinan-2019-build-it-break-it-fix/)
- [The Second Conversational Intelligence Challenge (ConvAI2)](https://md.research.elamur.ai/free/en/papers/dinan-2019-second-conversational-intelligence-challenge-convai2/)
- [Queens are Powerful too: Mitigating Gender Bias in Dialogue Generation](https://md.research.elamur.ai/free/en/papers/dinan-2020-queens-are-powerful-too-mitigating/)
- [Evaluating Large Language Models: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
- [Q²: Evaluating Factual Consistency in Knowledge-Grounded Dialogues via Question Generation and Question Answering](https://md.research.elamur.ai/free/en/papers/honovich-2021-q-2-evaluating-factual-consistency/)
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- [ProsocialDialog: A Prosocial Backbone for Conversational Agents](https://md.research.elamur.ai/free/en/papers/kim-2022-prosocialdialog-prosocial-backbone-conversational-agents/)
- [SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization](https://md.research.elamur.ai/free/en/papers/kim-2023-soda-million-scale-dialogue-distillation/)
- [Assessing Discourse Relations in Language Generation from GPT-2](https://md.research.elamur.ai/free/en/papers/ko-2020-assessing-discourse-relations-language-generation/)
- [Internet-Augmented Dialogue Generation](https://md.research.elamur.ai/free/en/papers/komeili-2022-internet-augmented-dialogue-generation/)
- [Don't Say That! Making Inconsistent Dialogue Unlikely with Unlikelihood Training](https://md.research.elamur.ai/free/en/papers/li-2020-don-t-say-that-making/)
- [LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods](https://md.research.elamur.ai/free/en/papers/li-2024-llms-judges-comprehensive-survey-llm/)
- [MoEL: Mixture of Empathetic Listeners](https://md.research.elamur.ai/free/en/papers/lin-2019-moel-mixture-empathetic-listeners/)
- [LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models](https://md.research.elamur.ai/free/en/papers/lin-2023-llm-eval-unified-multi-dimensional/)
- [Toxic Subword Pruning for Dialogue Response Generation on Large Language Models](https://md.research.elamur.ai/free/en/papers/lu-2026-toxic-subword-pruning-dialogue-response/)
- [Politeness Transfer: A Tag and Generate Approach](https://md.research.elamur.ai/free/en/papers/madaan-2020-politeness-transfer-tag-generate-approach/)
- [MIME: MIMicking Emotions for Empathetic Response Generation](https://md.research.elamur.ai/free/en/papers/majumder-2020-mime-mimicking-emotions-empathetic-response/)
- [DREAM: A Multicultural Multimodal Dataset Linking Dialogues and Realistic Image Sequences](https://md.research.elamur.ai/free/en/papers/mallo-2026-dream-multicultural-multimodal-dataset-linking/)
- [USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation](https://md.research.elamur.ai/free/en/papers/mehri-2020-usr-unsupervised-reference-free-evaluation/)
- [OpenDialKG: Explainable Conversational Reasoning with Attention-based Walks over Knowledge Graphs](https://md.research.elamur.ai/free/en/papers/moon-2019-opendialkg-explainable-conversational-reasoning-attention/)
- [Multi-dimensional Evaluation of Character-Authentic Dialogue Models Learned from Question-Answer Data](https://md.research.elamur.ai/free/en/papers/otsuka-2026-multi-dimensional-evaluation-character-authentic/)
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- [Recipes for Building an Open-Domain Chatbot](https://md.research.elamur.ai/free/en/papers/roller-2021-recipes-building-open-domain-chatbot/)
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- [Disentangling Indirect Answers to Yes-No Questions in Real Conversations](https://md.research.elamur.ai/free/en/papers/sanagavarapu-2022-disentangling-indirect-answers-yes-no/)
- [What makes a good conversation? How controllable attributes affect human judgments](https://md.research.elamur.ai/free/en/papers/see-2019-what-makes-good-conversation-how/)
- [Personality Traits in Large Language Models](https://md.research.elamur.ai/free/en/papers/serapio-2023-garcia-personality-traits-large-language-models/)
- [BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage](https://md.research.elamur.ai/free/en/papers/shuster-2022-blenderbot-3-deployed-conversational-agent/)
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- [CorefQA: Coreference Resolution as Query-based Span Prediction](https://md.research.elamur.ai/free/en/papers/wu-2020-corefqa-coreference-resolution-as-query/)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://md.research.elamur.ai/free/en/papers/xi-2023-rise-potential-large-language/)
- [Persona-Aware Evaluation of Cognitive Bias in LLMs: From Benchmark to Applied Decision-Making](https://md.research.elamur.ai/free/en/papers/yoshikawa-2026-persona-aware-evaluation-cognitive-bias/)
- [Knowledge-Grounded Dialogue Generation with Pre-trained Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2020-knowledge-grounded-dialogue-generation-pre/)
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- [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://md.research.elamur.ai/free/en/papers/zhou-2023-sotopia-interactive-evaluation-social-intelligence/)

## Cite

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

```bibtex
@inproceedings{zhang2018personalizing,
  title = {{Personalizing Dialogue Agents: I have a dog, do you have pets too?}},
  author = {Zhang, Saizheng and Dinan, Emily and Urbanek, Jack and Szlam, Arthur and Kiela, Douwe and Weston, Jason},
  booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2018},
  doi = {10.18653/v1/p18-1205},
  eprint = {1801.07243},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/p18-1205}
}
```

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