In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to multi-turn dialogue by modifying the state-of-the-art hredGAN architecture. To achieve this, we introduce an additional input modality into the encoder and decoder of hredGAN to capture other attributes such as speaker identity, location, sub-topics, and other external attributes that might be available from the corpus of human-to-human interactions. The resulting persona hredGAN ($phredGAN$) shows better performance than both the existing persona-based Seq2Seq and hredGAN models when those external attributes are available in a multi-turn dialogue corpus. This superiority is demonstrated on TV drama series with character consistency (such as Big Bang Theory and Friends) and customer service interaction datasets such as Ubuntu dialogue corpus in terms of perplexity, BLEU, ROUGE, and Distinct n-gram scores.

本文旨在通过引入额外的输入模式来捕捉多轮对话中的其他属性 (例如说话人身份、位置、子主题等), 来扩展基于人物的Seq2Seq神经网络对话模型，同时修改了现有的hredGAN架构。通过比较在多轮对话语料库(如Big Bang Theory 和 Friends的电视剧系列和Ubuntu对话语料库的客户服务交互数据集)上的表现，我们证明了 resulting persona hredGAN ($phredGAN$) 在当这些外部属性的情况下比现有的基于人物的Seq2Seq和hredGAN模型更优秀，表现出更好的对话生成效果和表达一致性。

对抗学习框架下的基于角色的多轮对话模型