---
title: "Will I Sound Like Me? Improving Persona — Kim 2020"
description: "Persona-based chatbots often say things that conflict with the persona they were given."
authors: ["Hyunwoo Kim", "Byeongchang Kim", "Gunhee Kim"]
year: 2020
venue: "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)"
source: "https://aclanthology.org/2020.emnlp-main.65/"
doi: "10.18653/v1/2020.emnlp-main.65"
arxiv: "2004.05816"
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/papers/kim-2020-will-i-sound-like-me/"
---
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# Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness

**Authors:** [Hyunwoo Kim](https://md.research.elamur.ai/free/authors/hyunwoo-kim/), [Byeongchang Kim](https://md.research.elamur.ai/free/authors/byeongchang-kim/), [Gunhee Kim](https://md.research.elamur.ai/free/authors/gunhee-kim/) · **Year:** 2020 · **Venue:** Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2020.emnlp-main.65/) · [PDF](https://aclanthology.org/2020.emnlp-main.65.pdf) · [DOI 10.18653/v1/2020.emnlp-main.65](https://doi.org/10.18653/v1/2020.emnlp-main.65) · [arXiv 2004.05816](https://arxiv.org/abs/2004.05816) · [OpenAlex](https://openalex.org/W3106007100)

**Citations:** 47 citations (openalex-snapshot · 2026-10-14) · [35](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Persona-based chatbots often say things that conflict with the persona they were given. Earlier fixes usually rely on NLI annotations or separately trained consistency modules, which are costly to build. The authors first show that three strong generators (ControlSeq2Seq, TransferTransfo, Blender) on the Dialogue NLI evaluation set pick contradictory candidates more often than ground-truth replies. These models also assign lower perplexity to contradicting words than to words the persona supports.

Inspired by the psychology of public self-consciousness, the authors add an imaginary listener at decoding time using the Rational Speech Acts (RSA) framework. The speaker favours tokens that would help this listener identify its own persona rather than distractor personas. They extend RSA in two ways:
- **Distractor Memory**, a learned method for choosing distractors instead of picking them manually or at random.
- **A new world-prior update** based on the literal listener, which better accumulates information across decoding steps.

Without extra labels or retraining, the method reduces contradictions and improves accuracy on Dialogue NLI and PersonaChat. It also improves context consistency beyond persona, for example on EmpatheticDialogue. This matters because the approach can be added to existing generators at low cost.

## Key points
- The Dialogue NLI evaluation set gives each dialogue 31 candidates: 10 entailing, 10 neutral, 10 contradictory and 1 ground-truth. Models are scored with Hits@1, Entail@1, Neutral@1 and Contradict@1.
- In base models, contradictory words had an average perplexity of about 106.7, versus 280.1 for their ground-truth counterparts. This suggests the models act more like plain language models than persona-conditioned ones.
- The self-conscious agent (S1) is built on top of a base speaker (S0) and works with any persona-based generator. It needs no consistency labels, NLI models or additional training.
- Updating the world prior with the literal listener L0 helped significantly, whereas using L1 performed about the same as a uniform prior. A listener rationality β ≤ 1 worked best.
- Speaker rationality α controls how much persona text is copied into replies. Very large values (e.g. α = 10) produce ungrammatical, persona-fragment outputs.

*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: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

35 papers of our library cite it (of the 140673 whose bibliographies we read); 4 of them have a page here, 31 not yet.

- [Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://md.research.elamur.ai/free/papers/fried-2023-pragmatics-language-grounding-phenomena-tasks/)
- [Perspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes](https://md.research.elamur.ai/free/papers/kim-2021-perspective-taking-pragmatics-generating-empathetic/)
- [Modeling Cross-Cultural Pragmatic Inference with Codenames Duet](https://md.research.elamur.ai/free/papers/shaikh-2023-modeling-cross-cultural-pragmatic-inference/)
- [RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework](https://md.research.elamur.ai/free/papers/wang-2024-rsa-control-pragmatics-grounded-lightweight/)

## Cite

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

```bibtex
@inproceedings{kim2020will,
  title = {{Will I Sound Like Me? Improving Persona Consistency in Dialogues through Pragmatic Self-Consciousness}},
  author = {Kim, Hyunwoo and Kim, Byeongchang and Kim, Gunhee},
  booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
  year = {2020},
  doi = {10.18653/v1/2020.emnlp-main.65},
  eprint = {2004.05816},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2020.emnlp-main.65}
}
```

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37. Thomas Wolf, Victor Sanh, Julien Chaumond, and Clement Delangue. 2019b. TransferTransfo: A Transfer Learning Approach for Neural Network based Conversational Agents. arXiv:1901.08149. — [arXiv:1901.08149](https://arxiv.org/abs/1901.08149) [find](https://scholar.google.com/scholar?q=Thomas+Wolf%2C+Victor+Sanh%2C+Julien+Chaumond%2C+and+Clement+Delangue%2E+2019b%2E+TransferTransfo%3A+A+Transfer+Learning+Approach+for+Neural+Network+based+Conversational+Agents%2E+arXiv%3A1901%2E08149%2E+%E2%80%94+%5BarXiv%3A1901%2E08)
38. Sina Zarrieß and David Schlangen. 2019. Know What You Don’t Know: Modeling a Pragmatic Speaker that Refers to Objects of Unknown Categories. In ACL. [find](https://scholar.google.com/scholar?q=Sina+Zarrie%C3%9F+and+David+Schlangen%2E+2019%2E+Know+What+You+Don%E2%80%99t+Know%3A+Modeling+a+Pragmatic+Speaker+that+Refers+to+Objects+of+Unknown+Categories%2E+In+ACL%2E) [our card](https://md.research.elamur.ai/free/papers/zarrie-2019-know-what-you-don-t/)
39. Saizheng Zhang, Emily Dinan, Jack Urbanek, Arthur Szlam, Douwe Kiela, and Jason Weston. 2018. Personalizing Dialogue Agents: I Have a Dog, Do You Have Pets Too? In ACL. [find](https://scholar.google.com/scholar?q=Saizheng+Zhang%2C+Emily+Dinan%2C+Jack+Urbanek%2C+Arthur+Szlam%2C+Douwe+Kiela%2C+and+Jason+Weston%2E+2018%2E+Personalizing+Dialogue+Agents%3A+I+Have+a+Dog%2C+Do+You+Have+Pets+Too%3F+In+ACL%2E) [our card](https://md.research.elamur.ai/free/papers/zhang-2018-personalizing-dialogue-agents-i-have/)
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