Generating responses that are consistent with the dialogue context is one of the central challenges in building engaging conversational agents. We demonstrate that neural conversation models can be geared towards generating consistent responses by maintaining certain features related to topics and personas throughout the conversation. Past work has required external supervision that exploits features such as user identities that are often unavailable. In our approach, topic and persona feature extractors are trained using a contrastive training scheme that utilizes the natural structure of dialogue data. We further adopt a feature disentangling loss which, paired with controllable response generation techniques, allows us to promote or demote certain learned topics and persona features. Evaluation results demonstrate the model's ability to capture meaningful topics and persona features. The incorporation of the learned features brings significant improvement in terms of the quality of generated responses on two dialogue datasets.

翻译：与对话环境相符合的响应是建立接触的交流媒介方面的主要挑战之一。我们证明神经对话模式可以通过在整个对话中保持与主题和个人有关的某些特征,从而产生一致的响应。过去的工作需要外部监督,利用经常无法获得的用户身份等特征。在我们的方法中,主题和个人特征提取器是使用利用对话数据的自然结构的对比性培训计划进行培训的。我们进一步采用了一种特征脱钩损失,与可控反应生成技术相结合,使我们能够促进或演示某些学习的专题和个人特征。评价结果表明该模式能够捕捉有意义的专题和个人特征。纳入这些学习的特征将极大地提高两个对话数据集生成的响应的质量。