Despite recent advancements, existing story generation systems continue to encounter difficulties in effectively incorporating contextual and event features, which greatly influence the quality of generated narratives. To tackle these challenges, we introduce a novel neural generation model, EtriCA, that enhances the relevance and coherence of generated stories by employing a cross-attention mechanism to map context features onto event sequences through residual mapping. This feature capturing mechanism enables our model to exploit logical relationships between events more effectively during the story generation process. To further enhance our proposed model, we employ a post-training framework for knowledge enhancement (KeEtriCA) on a large-scale book corpus. This allows EtriCA to adapt to a wider range of data samples. This results in approximately 5\% improvement in automatic metrics and over 10\% improvement in human evaluation. We conduct extensive experiments, including comparisons with state-of-the-art (SOTA) baseline models, to evaluate the performance of our framework on story generation. The experimental results, encompassing both automated metrics and human assessments, demonstrate the superiority of our model over existing state-of-the-art baselines. These results underscore the effectiveness of our model in leveraging context and event features to improve the quality of generated narratives.

翻译：尽管近期取得了进展，现有故事生成系统在有效整合上下文特征和事件特征方面仍面临困难，这极大地影响了生成叙事的质量。为解决这些挑战，我们提出了一种新颖的神经生成模型EtriCA，该模型通过残差映射将上下文特征映射到事件序列上，利用交叉注意力机制增强生成故事的相关性和连贯性。这种特征捕获机制使我们的模型能够在故事生成过程中更有效地利用事件间的逻辑关系。为进一步优化所提模型，我们在大规模图书语料库上采用了知识增强的后训练框架（KeEtriCA），使EtriCA能够适应更广泛的数据样本。这带来了自动评估指标约5%的提升以及人工评估超过10%的提升。我们开展了大量实验，包括与最先进的基线模型（SOTA）进行对比，以评估我们框架在故事生成任务上的性能。涵盖自动化指标和人工评估的实验结果表明，我们的模型优于现有最先进基线模型。这些结果充分证明了该模型在利用上下文和事件特征提升生成叙事质量方面的有效性。