---
title: "Personalizing Dialogue Agents via Meta-Learning — Madotto 2019"
description: "To build per-persona dialogue sets, the authors group Persona-chat… 88.43% accuracy on the DNLI. 37 references, cited by 65 papers here."
authors: ["Andrea Madotto", "Zhaojiang Lin", "Chien-Sheng Wu", "Pascale Fung"]
year: 2019
venue: "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics"
source: "https://aclanthology.org/P19-1542/"
doi: "10.18653/v1/p19-1542"
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/madotto-2019-personalizing-dialogue-agents-meta-learning/"
---
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# Personalizing Dialogue Agents via Meta-Learning

**Authors:** [Andrea Madotto](https://md.research.elamur.ai/free/en/authors/andrea-madotto/), [Zhaojiang Lin](https://md.research.elamur.ai/free/en/authors/zhaojiang-lin/), [Chien-Sheng Wu](https://md.research.elamur.ai/free/en/authors/chien-sheng-wu/) and [Pascale Fung](https://md.research.elamur.ai/free/en/authors/pascale-fung/) · **Year:** 2019 · **Venue:** [Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2019](https://md.research.elamur.ai/free/en/venues/acl/2019/)) · **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/P19-1542/) · [PDF](https://aclanthology.org/P19-1542.pdf) · [DOI 10.18653/v1/p19-1542](https://doi.org/10.18653/v1/p19-1542) · [OpenAlex](https://openalex.org/W2945978556)

**Citations:** 181 citations (OpenAlex, 2026-10-14) · [65](https://md.research.elamur.ai/free/en/papers/madotto-2019-personalizing-dialogue-agents-meta-learning/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Personalized chit-chat models usually make responses consistent by conditioning them on short, hand-written persona profiles. Such profiles are costly to produce and hard to extract from real conversations. Madotto et al. avoid profiles entirely. They treat each persona as a separate task and apply Model-Agnostic Meta-Learning (MAML). Their variant, Persona-Agnostic Meta-Learning (PAML), learns initial weights for a Transformer response generator so that a few dialogues from one user are enough to fine-tune it toward that user's style.

To build per-persona dialogue sets, the authors group Persona-chat conversations by their persona description. Evaluation combines four signals:
- perplexity and BLEU;
- a new consistency score C, computed with a BERT model fine-tuned on Dialog NLI;
- crowd-sourced human ratings.

PAML is the most persona-consistent system in both C and human judgments, and humans rate its fluency as comparable to the baselines. It does have weaker perplexity and BLEU. It also outperforms a model that sees the persona description directly. The result suggests that personalization can come from a user's past conversations rather than from manually written descriptions.

## Key points
- **Data:** On average, each persona in Persona-chat has 8.3 unique dialogues. At test time, the model is fine-tuned on all of a persona's dialogues except one, and the held-out dialogue is used for evaluation.
- **Consistency metric C:** Each generated utterance is scored +1 if it entails a persona sentence, 0 if neutral, and −1 if it contradicts one, summed over the persona sentences. The BERT classifier behind it reaches 88.43% accuracy on the DNLI test set, on par with ESIM's 88.20%.
- **Automatic results (C / PPL / BLEU):**

  | System | C | PPL | BLEU |
  |---|---|---|---|
  | PAML | 0.20 | 41.64 | 0.74 |
  | Dialogue+Persona | 0.07 | 30.42 | 1.00 |
  | Dialogue+Fine-tuning | 0.00 | 32.96 | 0.90 |
  | Human reference | 0.33 | — | — |

- **Human evaluation:** 300 responses from 10 personas were rated. PAML scored a fluency of 3.185 and a consistency of 0.197. Dialogue+Persona scored 3.053 fluency and 0.011 consistency.
- **Few-shot behaviour and code:** PAML exceeds Dialogue+Persona in consistency with just 3 dialogues, and its consistency grows roughly linearly with k. Plain fine-tuning barely benefits from extra dialogues. The model uses GloVe embeddings, SGD in the inner loop (α=0.01), and Adam in the outer loop (β=0.0003). Code is available at github.com/HLTCHKUST/PAML.

*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

- [Hong Kong University of Science and Technology](https://md.research.elamur.ai/free/en/universities/hong-kong-university-of-science-and-technology/) — University, Hong Kong, China · 58 works · 23,058 citations

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## Cite

Cite as: Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu and Pascale Fung. 2019. Personalizing Dialogue Agents via Meta-Learning. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics.

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

```bibtex
@inproceedings{madotto2019personalizing,
  title = {{Personalizing Dialogue Agents via Meta-Learning}},
  author = {Andrea Madotto, Zhaojiang Lin, Chien-Sheng Wu and Pascale Fung},
  booktitle = {Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics},
  year = {2019},
  doi = {10.18653/v1/p19-1542},
  url = {https://doi.org/10.18653/v1/p19-1542}
}
```

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