A Persona-Based Neural Conversation Model

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.

• Publication year

  2016

• Venue

  Annual Meeting of the Association for Computational Linguistics

• Publication date

  2016-03-19

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.18653/v1/P16-1094arXiv 1603.06155
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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