A Persona-Based Neural Conversation Model

Understand

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.