Source-linked AI summary
Personalizing Dialogue Agents via Meta-Learning
Zhaojiang Lin, Andrea Madotto, Chien-Sheng Wu, Pascale Fung
TL;DR
Existing personalized dialogue systems rely on persona descriptions that are difficult and costly to obtain. This paper applies meta-learning so agents adapt to new personas from a few dialogues, achieving more consistent dialogue by automatic measures and human evaluation.
Problem
Persona descriptions are difficult to capture from few sentences and costly to collect from real conversations because they require hand-crafted feature designs.
Method
Persona-Agnostic Meta-Learning (PAML) learns initial dialogue-model parameters that quickly adapt to a persona using a few dialogues, without conditioning responses on persona sentences.
Results
Meta-learning achieves more consistent dialogue than baselines according to automatic measures and human evaluation, with human-evaluated consistency aligned with the C score.
Takeaways & Limitations
Personalized dialogue agents can use a few dialogues directly instead of human-designed persona descriptions while improving consistency across automatic and human evaluation.
Abstract
from arXiv · showhide
Existing personalized dialogue models use human designed persona descriptions to improve dialogue consistency. Collecting such descriptions from existing dialogues is expensive and requires hand-crafted feature designs. In this paper, we propose to extend Model-Agnostic Meta-Learning (MAML)(Finn et al., 2017) to personalized dialogue learning without using any persona descriptions. Our model learns to quickly adapt to new personas by leveraging only a few dialogue samples collected from the same user, which is fundamentally different from conditioning the response on the persona descriptions. Empirical results on Persona-chat dataset (Zhang et al., 2018) indicate that our solution outperforms non-meta-learning baselines using automatic evaluation metrics, and in terms of human-evaluated fluency and consistency.
1 Introduction
Personalized dialogue agents can improve consistency through persona information, but obtaining useful persona descriptions is difficult and labor-intensive. The paper instead frames personalization as few-shot meta-learning, enabling rapid adaptation from dialogues without human-designed persona descriptions.
- Persona-chat assigns each speaker a 4–5 sentence persona description, which can condition response generation for more persona-consistent dialogue.
- Capturing a persona with only a few sentences is difficult, while collecting descriptions from real conversations requires hand-crafted feature designs.
- The proposed approach leverages dialogues from the same persona directly instead of using persona descriptions to generate more consistent responses.
- Personalized dialogue learning is formulated as few-shot meta-learning, learning persona-independent parameters that quickly adapt to a new persona from a few dialogues.
- The main contribution is generating personalized responses from a few dialogue samples rather than human-designed persona descriptions.
- Human evaluations show the solution outperforms joint training in fluency and consistency.
2 Personalized Dialogue Learning
The paper frames personalized dialogue learning as a meta-learning problem: models adapt to personas from a few dialogues rather than conditioning responses on persona descriptions. PAML trains an initial model across persona-specific tasks and evaluates adapted models on unseen dialogues.
- Persona-conditioned dialogue: A personalized dialogue model produces a response from previous utterances and persona sentences, formalizing persona-conditioned dialogue generation.The response is Y = u_t, conditioned on dialogue history X and persona description P.
- Persona-agnostic dialogue: PAML instead adapts model parameters using a persona’s dialogue set, then generates responses using only the dialogue history.This replaces direct persona-description conditioning with adaptation from dialogue samples.
- Persona-agnostic dialogue: Personas are treated as separate tasks in a few-shot meta-learning problem, with an initial model optimized to adapt quickly to new personas.Each persona’s dialogues are divided into training and validation sets for adaptation and evaluation.
- Persona-agnostic dialogue: The training procedure uses persona batches, separate dialogue training and validation samples, and step sizes α and β for inner and meta optimization.Algorithm 1 summarizes the iterative procedure and requires both step-size hyperparameters.
- Persona-agnostic dialogue: The inner optimization updates persona-specific parameters by gradient descent using cross-entropy response-generation loss.The adapted model is then evaluated on unseen dialogues, and the meta-model is optimized for that adapted performance.
- Evaluation: Table 1 compares PAML with Dialogue+Persona and Dialogue+Fine-tuning to assess consistency and the effectiveness of meta-learning personalization.The comparison specifically tests dialogue-based personalization against persona-description conditioning and fine-tuning.
3 Experiment and Results
Experiments on Persona-chat evaluate PAML against dialogue-based baselines using automatic and human measures, including few-shot adaptation. PAML achieves stronger consistency, while human-rated fluency remains comparable across settings.
- Experimental setting: Persona-chat meta-sets match dialogues by persona description across the original train, validation, and test splits, with 8.3 unique dialogues per persona on average.
- Experimental setting: The evaluation compares dialogue-only training, PAML meta-training, and dialogue models with persona-specific fine-tuning.
- Evaluation: The study uses perplexity, BLEU, and an NLI-based consistency score, supplemented by human ratings of fluency and persona consistency.
- Results: PAML achieves better dialogue consistency in both automatic and human evaluation, while human-rated fluency is comparable across settings.
- Results: Human consistency ratings align with the NLI-based C score, supporting the meaningfulness of the defined consistency measure.
- Few-shot learning: PAML reaches high consistency with three dialogue shots, whereas dialogue-only fine-tuning cannot properly leverage the persona dialogues.
4 Related Work
Prior work applied meta-learning mainly to vision and several NLP tasks, while personalized dialogue systems generally conditioned responses on persona descriptions. The paper positions PAML as an initial meta-learning application to personalized dialogue learning.
- Meta-Learning: Meta-learning has been studied for few-shot image classification, optimization, and reinforcement learning.
- Meta-Learning: NLP applications of meta-learning include semantic parsing, low-resource machine translation, and text classification.
- Meta-Learning: The paper describes its approach as the first attempt to adapt meta-learning to personalized dialogue learning.
- Personalized Dialogue: Earlier personalized dialogue systems improved consistency while conditioning responses on persona descriptions.
5 Conclusion
The paper presents meta-learning for personalizing dialogue agents without conditioning responses on persona descriptions. It reports improved consistency under automatic and human evaluation and identifies comment generation and task-oriented dialogue as future applications.
- Conclusion: The proposed setting personalizes dialogue agents without conditioning model responses on persona descriptions.
- Conclusion: The authors report that meta-learning produces more consistent dialogue under both automatic measures and human evaluation.
- Conclusion: Future work will apply meta-learning to comment generation and task-oriented dialogue systems.
A.2 Plots
The plots describe the training-data distribution and show how PAML consistency changes with fine-tuning iterations. The supplied captions state linear growth with iteration but provide no numeric values.
- Figure 3: Figure 3 presents the distribution of dialogue counts for each persona description in the training set.
- Figure 4: Figure 4 plots fine-tuning iteration against consistency and states that PAML consistency grows linearly with iteration.
A.3 Human evaluation
Human evaluation asks crowd workers to judge generated responses for fluency and persona consistency using dialogue context and persona descriptions. Consistency distinguishes contradictions, relevant persona-aligned answers, and neutral responses.
- Evaluation setup: Crowd workers evaluate each generated response for fluency and consistency after viewing the dialogue history, persona description, and response.The evaluation presents one response from an evaluated setting together with its conversational and persona context.
- Consistency criterion: A response is consistent when it neither contradicts the dialogue history or persona description nor lacks relevance to the persona description.This operational definition requires both non-contradiction and relevance to at least one persona sentence.
- Neutral responses: Generic or persona-irrelevant answers are neutral when they do not contradict either the dialogue history or persona description.For example, discussing swimming when the persona mentions basketball is neutral, whereas discussing basketball is consistent and rejecting basketball is contradictory.
- Consistency criterion: Consistency scores use three labels: contradiction is -1, neutrality is 0, and consistency is 1.The scoring scale explicitly separates contradictory, neutral, and consistent answers.
- Evaluation examples: The evaluation examples pair persona statements such as liking autumn, owning a turtle, or being a student with dialogue histories involving work, education, and family responsibilities.These examples provide the contextual material against which generated responses are judged.