Building a persona-based conversation agent is challenging owing to the lack
of large amounts of speaker-specific conversation data for model training. This
paper addresses the problem by proposing a multi-task learning approach to
training neural conversation models that leverages both conversation data
across speakers and other types of data pertaining to the speaker and speaker
roles to be modeled. Experiments show that our approach leads to significant
improvements over baseline model quality, generating responses that capture
more precisely speakers' traits and speaking styles. The model offers the
benefits of being algorithmically simple and easy to implement, and not relying
on large quantities of data representing specific individual speakers.

本文提出了一种使用多任务学习方法训练神经会话模型的方式来解决根据具体发言人训练缺乏数据、建立基于角色的会话代理的问题。实验证明该方法显著提高了基线模型的质量，生成更准确地捕捉发言者特征和讲话风格的响应，并且该方法算法简单易实现，不依赖于代表特定个体发言者的大量数据。