论文标题
学习编码自动评论长篇小说的进化知识
Learning to Encode Evolutionary Knowledge for Automatic Commenting Long Novels
论文作者
论文摘要
静态知识图已广泛地纳入文本生成的序列到序列框架中。尽管有效地表示结构化的上下文，但静态知识图无法表示知识的演变，这在建模动态事件时是必需的。在本文中，为长小说提出了自动评论任务，其中涉及了解成千上万个单词的上下文。为了建模动态故事情节，尤其是角色的过渡及其关系，在多任务框架内提出并学习了进化知识图（EKG）。给定特定的评论段落，顺序建模用于将历史和未来嵌入到上下文表示。此外，图表到序列模型旨在利用心电图进行评论。广泛的实验结果表明，我们基于心电的方法在自动和人类评估方面都优于几种强大的基准。
Static knowledge graph has been incorporated extensively into sequence-to-sequence framework for text generation. While effectively representing structured context, static knowledge graph failed to represent knowledge evolution, which is required in modeling dynamic events. In this paper, an automatic commenting task is proposed for long novels, which involves understanding context of more than tens of thousands of words. To model the dynamic storyline, especially the transitions of the characters and their relations, Evolutionary Knowledge Graph(EKG) is proposed and learned within a multi-task framework. Given a specific passage to comment, sequential modeling is used to incorporate historical and future embedding for context representation. Further, a graph-to-sequence model is designed to utilize the EKG for comment generation. Extensive experimental results show that our EKG-based method is superior to several strong baselines on both automatic and human evaluations.
