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.