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.
@article{arxiv.1603.06155,
title = {A Persona-Based Neural Conversation Model},
author = {Jiwei Li and Michel Galley and Chris Brockett and Georgios P. Spithourakis and Jianfeng Gao and Bill Dolan},
journal= {arXiv preprint arXiv:1603.06155},
year = {2016}
}