第一人称叙事中主人公目标和欲望的建模
Modelling Protagonist Goals and Desires in First-Person Narrative
摘要：
自然语言文本的许多类型都是叙事性结构的，这证明了我们偏好将我们的经验组织为叙事。人们普遍认为，理解一个叙事需要识别和跟踪人物的目标和愿望及其叙事结果。然而，迄今为止，已经有有限的工作计算模型，这个问题。我们引入了一个新的数据集，DesireDB，其中包括黄金标准的标签，用于识别欲望的陈述，欲望实现的文本证据，以及在叙事背景下给出的证据是否满足所述欲望的注释。我们报告了使用不同方法跟踪愿望实现的实验，并表明LSTM Skip-Thought模型在我们的语料库上实现了0.7的F-测量。
英文摘要：
Many genres of natural language text are narratively structured, a testament to our predilection for organizing our experiences as narratives. There is broad consensus that understanding a narrative requires identifying and tracking the goals and desires of the characters and their narrative outcomes. However, to date, there has been limited work on computational models for this problem. We introduce a new dataset, DesireDB, which includes gold-standard labels for identifying statements of desire, textual evidence for desire fulfillment, and annotations for whether the stated desire is fulfilled given the evidence in the narrative context. We report experiments on tracking desire fulfillment using different methods, and show that LSTM Skip-Thought model achieves F-measure of 0.7 on our corpus.
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。
