Personalizing Dialogue Agents: I have a dog, do you have pets too? (1801.07243v5)

Abstract: Chit-chat models are known to have several problems: they lack specificity, do not display a consistent personality and are often not very captivating. In this work we present the task of making chit-chat more engaging by conditioning on profile information. We collect data and train models to (i) condition on their given profile information; and (ii) information about the person they are talking to, resulting in improved dialogues, as measured by next utterance prediction. Since (ii) is initially unknown our model is trained to engage its partner with personal topics, and we show the resulting dialogue can be used to predict profile information about the interlocutors.

Personalizing Dialogue Agents: "I have a dog, do you have pets too?"

In the paper "Personalizing Dialogue Agents: I have a dog, do you have pets too?" by Saizheng Zhang et al., the authors propose and evaluate techniques for enhancing chit-chat models by incorporating persona information. The research specifically addresses common challenges in dialogue technology, such as consistency, engagement, and specificity by conditioning on user profiles.

Introduction

Current chit-chat models suffer from vagueness, lack of coherent personality, and overall unengagement. This work introduces techniques to mitigate these issues, including the creation of the Persona-Chat dataset designed to help train dialogue models that condition on profile information. These profiles enable the agents to ask and answer personal questions, subsequently enriching conversational quality.

Data Collection and Dataset Overview

The Persona-Chat dataset was constructed through a multi-stage process involving:

1. The creation of 1155 unique personas with multiple descriptive sentences, which were verified for diversity and relevance.
2. These personas were then rewritten to avoid word overlaps that may facilitate trivial matching, creating revised versions for more robust learning.
3. Finally, dialogues were collected by pairing crowdworkers who role-played using these personas, generating over 160,000 utterances.

This dataset is open-source and available in ParlAI—a framework for dialogue AI research, promoting further experimentation in dialogue personalization.

Models and Methods

The paper employs both generative and ranking model approaches. Key models include:

• Seq2Seq: A standard neural model that appends persona information directly to the input sequence, generating responses word-by-word.
• Profile Memory Network: A memory-augmented model that attends over persona information vectors to condition responses, enhancing dialogue coherence and relevance.
• Key-Value Profile Memory Network: An advanced architecture that uses historical dialogue exchanges to derive keys for more contextually aware responses, showing robust performance in ranking tasks.

The paper also compares against traditional IR baselines like tf-idf and cutting-edge embedding models like Starspace, underscoring the contribution of persona conditioning on model performance.

Results

Performance evaluations are conducted using perplexity, F1 score, and hits@1 metrics. Key findings include:

• Models conditioned on their own persona ("Self Persona") showed marked improvements in specificity and engagement over non-conditioned models, especially under original persona settings.
• Ranking models—notably the Key-Value Profile Memory Network—outperformed generative models in next utterance prediction tasks, though generative models showed potential in capturing personalized nuances.
• Training on the revised personas without word overlap demonstrated enhanced model generalization, suggesting robustness against overfitting to static phrases.

Human Evaluation

The extrinsic evaluation involved human raters interacting with the models, assessing fluency, engagement, consistency, and the detectability of the model’s persona. Persona-conditioned models generally received higher consistency scores, although engagement scores showed variability likely due to differences in conversational dynamics and persona appropriateness.

Implications and Future Directions

The results emphasize the importance of personalized dialogue agents in improving human-computer interaction quality. Implications include:

• Enhanced user experience through more relatable and contextually aware conversations.
• Potential applications in customer service, personal assistant technologies, and social robots where maintaining user interest and relevance is paramount.

Future research could explore dynamic persona updating and multi-turn persona adaptation, thereby further refining the coherence and richness of AI-driven dialogues. Combining Task 1 (next utterance prediction) and Task 2 (profile prediction) into a seamless conversational experience represents a promising avenue for advancing dialogue systems.

Conclusion

This paper advances the field of dialogue systems by demonstrating the efficacy of incorporating persona information. The careful construction of the Persona-Chat dataset and the robust experimentation with diverse models lay a solid groundwork for developing consistently engaging dialogue agents. Enhanced personalization through structured profile conditioning marks a significant step towards more naturalistic and user-centered AI interactions.