A Persona-Based Neural Conversation Model (1603.06155v2)

Abstract: We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speaker-addressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models, with similar gains in speaker consistency as measured by human judges.

A Persona-Based Neural Conversation Model

This paper presents innovative persona-based models intended to address the issue of speaker consistency in neural response generation. Given the increasing role of conversational agents as user interfaces, the authors focus on endowing these systems with coherent persona characteristics. The models introduced include a Speaker Model, which encodes individual characteristics of the speaker, and a Speaker-Addressee Model, which captures interaction properties between two interlocutors. This work fundamentally builds upon the sequence-to-sequence (Seq2Seq) framework, enhancing it with persona embeddings that effectively incorporate speaker-specific information.

Proposed Models

Speaker Model

The Speaker Model integrates speaker-level vector representations into the Seq2Seq architecture. By encoding each speaker persona into embeddings, this model is able to capture speaker-specific information, such as background facts and speaking style. This approach yields a $21.7\%$ improvement in BLEU scores using Maximum Likelihood Estimation (MLE) and $11.7\%$ using Maximum Mutual Information (MMI) over standard Seq2Seq models, showcasing a notable enhancement in the generation of personalized responses.

Speaker-Addressee Model

Extending the Speaker Model, the Speaker-Addressee Model aims to model the interaction between a speaker and an addressee. This dyadic model constructs an interaction representation from their individual embeddings and incorporates it into the Seq2Seq framework. The model addresses the variability of responses based on the interlocutor's identity—a phenomenon known as lexical entrainment. Although qualitative gains were noted, the performance improvement in this model, particularly in datasets with smaller scales, underlines the potential and challenges of capturing intricate dialog dynamics.

Datasets and Training Protocols

The research utilizes two primary datasets for evaluating the proposed models: the Twitter Persona Dataset and Television Series Transcripts from "Friends" and "The Big Bang Theory."

1. Twitter Persona Dataset:
   • Comprising responses from 74,003 users engaged in 3-turn conversational snippets.
   • Trained using a 4-layer LSTM with a vocab size of 50,000, the model achieved a $10.6\%$ reduction in perplexity compared to the standard Seq2Seq model.
2. Television Series Dataset:
   • Included scripts from primary characters, trained initially on the OpenSubtitles (OSDb) dataset, followed by domain adaptation to TV series data.
   • The Speaker and Speaker-Addressee models recorded a perplexity of 25.4 and 25.0 respectively, compared to 27.3 for the standard model, with a corresponding gain in BLEU scores.

Evaluation and Results

Both numerical and qualitative evaluations are provided:

• Perplexity and BLEU Scores: Across datasets, persona-based models demonstrate improved performance in perplexity and BLEU scores relative to standard LSTM models. Specifically, relative improvements of up to $20\%$ in BLEU scores and $12\%$ in perplexity metrics underscore the models' efficacy.
• Human Evaluation: Human annotators preferred the Speaker Model $56.7\%$ of the time for consistency, evidencing the practical benefit of persona embeddings.

Practical and Theoretical Implications

The practical implications of this research are significant. By enhancing the coherence and personalization of responses, these models can dramatically improve user interactions with conversational agents. They enable the development of more relatable and context-aware chatbots, personal assistants, and NPCs in gaming environments.

Theoretically, the approach represents a substantial step towards personalized conversation generation, aiding the broader understanding of persona dynamics in neural models. Future developments may integrate richer dimensions of speaker behavior, such as mood and emotion, and further investigate dyadic interaction models on larger datasets.

Conclusion

The paper successfully demonstrates that integrating persona vectors into neural conversation models enhances speaker consistency and personalization. While the improvements are incremental, the robust methodology and the consistent performance gains pave the way for future explorations in personalized AI-driven conversation systems. Such advancements promise to enrich human-machine interactions, fostering the creation of more engaging and contextually aware conversational agents.