语义框架预测
Semantic Frame Forecast
摘要：
本文介绍了语义框架预测，一个任务，预测语义框架，将出现在未来10个，100个，甚至1,000个句子的故事。之前的工作主要集中在预测故事的近期未来，例如前面的一到几句话。然而，当小说家写长篇故事时，生成几个句子并不足以帮助他们获得高层次的洞察力来发展后续故事。在本文中，我们将一个长篇故事表述为一系列“故事块”，其中每个故事块包含固定数量的句子（例如，10、100或200）。这个公式使我们能够预测超出几句话范围的后续故事弧线。我们表示一个故事块中使用的术语频率（TF）的语义框架，规范化的每个帧的逆文档频率（IDF）。我们对来自Bookcorpus的4，794本书和来自CODA-19的7，962篇科学摘要进行了语义框架预测实验，块大小从5到1，000句不等。结果表明，自动模型可以预测后续的故事块比随机，先验和重放基线，表明任务的可行性。我们还了解到，当块大小超过150个句子时，使用帧表示作为特征的模型优于所有现有的方法。人类的评价也表明，所提出的框架表示，当可视化为词云，是可理解的，代表性的，和特定的人类。我们的代码可在https://github.com/appleternity/FrameForecasting上获得。
英文摘要：
This paper introduces semantic frame forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. Prior work focused on predicting the immediate future of a story, such as one to a few sentences ahead. However, when novelists write long stories, generating a few sentences is not enough to help them gain high-level insight to develop the follow-up story. In this paper, we formulate a long story as a sequence of "story blocks," where each block contains a fixed number of sentences (e.g., 10, 100, or 200). This formulation allows us to predict the follow-up story arc beyond the scope of a few sentences. We represent a story block using the term frequencies (TF) of semantic frames in it, normalized by each frame's inverse document frequency (IDF). We conduct semantic frame forecast experiments on 4,794 books from the Bookcorpus and 7,962 scientific abstracts from CODA-19, with block sizes ranging from 5 to 1,000 sentences. The results show that automated models can forecast the follow-up story blocks better than the random, prior, and replay baselines, indicating the task's feasibility. We also learn that the models using the frame representation as features outperform all the existing approaches when the block size is over 150 sentences. The human evaluation also shows that the proposed frame representation, when visualized as word clouds, is comprehensible, representative, and specific to humans. Our code is available at https://github.com/appleternity/FrameForecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
