论文标题
通过将他们分解为叙事条款类型，探索说话个人叙事之间相似性的各个方面
Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types
论文作者
论文摘要
分享个人叙事是人类社会行为的一个基本方面，因为它有助于分享我们的生活经验。我们可以讲故事并依靠我们的背景来了解他们的背景，相似性和差异。为开发讲故事的机器或推断角色的功能做出了巨大的努力。但是，我们通常找不到比较叙事的模型。对于机器而言，这项任务非常具有挑战性，因为它们有时候我们缺乏对相似性含义的理解。为了应对这一挑战，我们首先介绍了一个现实世界中的个人叙事语料库，其中包括594个视频成绩单中的10,296个叙事条款。其次，我们要求非叙事专家根据Labov的个人叙事社会语言模型（即行动，方向和评估条款类型）注释这些条款，并培训达到最高符合条款的F-评分的分类器。最后，我们匹配故事，并探索人们是否隐含地依靠拉伯夫的框架来比较叙事。我们表明，叙述者对这些的评估之后是非专家认为最考虑的方面。我们的方法旨在帮助旨在研究或代表个人叙事的机器学习方法。
Sharing personal narratives is a fundamental aspect of human social behavior as it helps share our life experiences. We can tell stories and rely on our background to understand their context, similarities, and differences. A substantial effort has been made towards developing storytelling machines or inferring characters' features. However, we don't usually find models that compare narratives. This task is remarkably challenging for machines since they, as sometimes we do, lack an understanding of what similarity means. To address this challenge, we first introduce a corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts. Second, we ask non-narrative experts to annotate those clauses under Labov's sociolinguistic model of personal narratives (i.e., action, orientation, and evaluation clause types) and train a classifier that reaches 84.7% F-score for the highest-agreed clauses. Finally, we match stories and explore whether people implicitly rely on Labov's framework to compare narratives. We show that actions followed by the narrator's evaluation of these are the aspects non-experts consider the most. Our approach is intended to help inform machine learning methods aimed at studying or representing personal narratives.
