  Dialogue generation models face the challenge of producing generic and
repetitive responses. Unlike previous augmentation methods that mostly focus on
token manipulation and ignore the essential variety within a single sample
using hard labels, we propose to promote the generation diversity of the neural
dialogue models via soft embedding augmentation along with soft labels in this
paper. Particularly, we select some key input tokens and fuse their embeddings
together with embeddings from their semantic-neighbor tokens. The new
embeddings serve as the input of the model to replace the original one.
Besides, soft labels are used in loss calculation, resulting in multi-target
supervision for a given input. Our experimental results on two datasets
illustrate that our proposed method is capable of generating more diverse
responses than raw models while remains a similar n-gram accuracy that ensures
the quality of generated responses.
