Abstract
EMNLP 2022 Findings One of the key challenges of automatic story generation is how to generate a
long narrative that can maintain fluency, relevance, and coherence. Despite
recent progress, current story generation systems still face the challenge of
how to effectively capture contextual and event features, which has a profound
impact on a model's generation performance. To address these challenges, we
present EtriCA, a novel neural generation model, which improves the relevance
and coherence of the generated stories through residually mapping context
features to event sequences with a cross-attention mechanism. Such a feature
capturing mechanism allows our model to better exploit the logical relatedness
between events when generating stories. Extensive experiments based on both
automatic and human evaluations show that our model significantly outperforms
state-of-the-art baselines, demonstrating the effectiveness of our model in
leveraging context and event features.