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 speakeraddressee 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.
REF encoded personas of individuals in contextualized embeddings that capture the background information and style to maintain consistency in the responses given.
A Persona-Based Neural Conversation Model
ACL
Computer Science