Conversational responses are non-trivial for artificial conversational agents. Artificial responses should not only be meaningful and plausible, but should also (1) have an emotional context and (2) should be non-deterministic (i.e., vary given the same input). The two factors enumerated, respectively, above are involved and this is demonstrated such that previous studies have tackled them individually. This paper is the first to tackle them together. Specifically, we present two models both based upon conditional variational autoencoders. The first model learns disentangled latent representations to generate conversational responses given a specific emotion. The other model explicitly learns different emotions using a mixture of multivariate Gaussian distributions. Experiments show that our proposed models can generate more plausible and diverse conversation responses in accordance with designated emotions compared to baseline approaches.

对于人工对话代理来说，对话响应是很重要的。人为的回应不仅应该是有意义和合理的，而且还应该（1）具有情感背景，并且（2）应该是不确定的（即，在相同的输入下有所不同）。以上分别列举了两个因素，事实证明，以前的研究已经分别解决了这两个因素。本文是第一个一起解决它们的方法。具体来说，我们提出了两种均基于条件变分自动编码器的模型。第一个模型学习纠缠的潜在表示，以在给定特定情感的情况下生成对话响应。另一个模型使用多元高斯分布的混合显式地学习不同的情绪。