| dc.description.abstract | Dialogue generation technology has shown great potential, but currently dialogue systems
usually generate plain and general responses. Directly allowing the dialogue system to produce
stylized responses a solution that allow the dialogue systems to generate diversified responses.
In this study, we propose a dialogue generation method with styles. While generating dialogues,
we can do style conversion to achieve multiple styles of responses to a question. The purpose
is to allow the machine to respond with appropriate styles in different dialogue scenarios. This
task can also be said to be an effective combination of dialogue generation tasks and style
conversion tasks, so we not only attach importance to reply sentences to be able to respond
appropriately, but also emphasize the ability to reply with high style intensity.
With the data characteristics, the dialogue dataset is usually parallel data, one context has
a corresponding response, and the style text dataset is usually non-parallel, so we use supervised
learning to construct the dialogue generation model, using unsupervised learning method
constructs a style transfer model, and makes the two models shared a decoder and combine
them into a hybrid model. We propose using lightweight deep neural network models to bridge
the latent spaces of dialogue response generation model and style transfer model. This structure
allows the model to generate many different impressive style sentences. In chapter four, we
show the results of successfully bridging the latent space of dialogue generation to the latent
space of style transfer, and we use multiple auto evaluation metrics and human evaluation to
compare the effectiveness of our proposed model with the benchmark model in many aspects.
The results indicate that the style intensity and the fluency of sentences are much better than
that of the benchmark model, and the appropriateness of the responses is maintained
comparable to that of the benchmark model. Not only that, we use two external dialogue
datasets to test the applicability of our model. The results show that the text used for daily
dialogue has good applicability. | en_US |