Abstract
Automatic story ending generation is an interesting and challenging task in natural language
generation. Previous studies are mainly limited to generate coherent, reasonable and diversified story endings, and few works focus
on controlling the sentiment of story endings.
This paper focuses on generating a story ending which meets the given fine-grained sentiment intensity. There are two major challenges
to this task. First is the lack of story corpus
which has fine-grained sentiment labels. Second is the difficulty of explicitly controlling
sentiment intensity when generating endings.
Therefore, we propose a generic and novel
framework which consists of a sentiment analyzer and a sentimental generator, respectively
addressing the two challenges. The sentiment
analyzer adopts a series of methods to acquire sentiment intensities of the story dataset.
The sentimental generator introduces the sentiment intensity into decoder via a Gaussian
Kernel Layer to control the sentiment of the
output. To the best of our knowledge, this is
the first endeavor to control the fine-grained
sentiment for story ending generation without manually annotating sentiment labels. Experiments show that our proposed framework
can generate story endings which are not only
more coherent and fluent but also able to meet
the given sentiment intensity better