  In human dialogue, a single query may elicit numerous appropriate responses.
The Transformer-based dialogue model produces frequently occurring sentences in
the corpus since it is a one-to-one mapping function. CVAE is a technique for
reducing generic replies. In this paper, we create a new dialogue model
(CVAE-T) based on the Transformer with CVAE structure. We use a pre-trained MLM
model to rewrite some key n-grams in responses to obtain a series of negative
examples, and introduce a regularization term during training to explicitly
guide the latent variable in learning the semantic differences between each
pair of positive and negative examples. Experiments suggest that the method we
design is capable of producing more informative replies.

Computation and Language;Machine Learning