Abstract
AACL 2022 To improve the performance of long text generation, recent studies have
leveraged automatically planned event structures (i.e. storylines) to guide
story generation. Such prior works mostly employ end-to-end neural generation
models to predict event sequences for a story. However, such generation models
struggle to guarantee the narrative coherence of separate events due to the
hallucination problem, and additionally the generated event sequences are often
hard to control due to the end-to-end nature of the models. To address these
challenges, we propose NGEP, an novel event planning framework which generates
an event sequence by performing inference on an automatically constructed event
graph and enhances generalisation ability through a neural event advisor. We
conduct a range of experiments on multiple criteria, and the results
demonstrate that our graph-based neural framework outperforms the
state-of-the-art (SOTA) event planning approaches, considering both the
performance of event sequence generation and the effectiveness on the
downstream task of story generation.