  Story generation aims to generate a long narrative conditioned on a given
input. In spite of the success of prior works with the application of
pre-trained models, current neural models for Chinese stories still struggle to
generate high-quality long text narratives. We hypothesise that this stems from
ambiguity in syntactically parsing the Chinese language, which does not have
explicit delimiters for word segmentation. Consequently, neural models suffer
from the inefficient capturing of features in Chinese narratives. In this
paper, we present a new generation framework that enhances the feature
capturing mechanism by informing the generation model of dependencies between
words and additionally augmenting the semantic representation learning through
synonym denoising training. We conduct a range of experiments, and the results
demonstrate that our framework outperforms the state-of-the-art Chinese
generation models on all evaluation metrics, demonstrating the benefits of
enhanced dependency and semantic representation learning.
