Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives
Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives
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DOI：
10.18653/v1/n19-1052
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发表时间：
2019-04
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影响因子：
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通讯作者：
Liye Fu;Jonathan P. Chang;Cristian Danescu-Niculescu-Mizil
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作者：
Liye Fu;Jonathan P. Chang;Cristian Danescu-Niculescu-Mizil
人们经常分享个人叙述以寻求他人的建议。要正确推断叙述者的意图，需要运用一定程度的常识和社会直觉。为了测试 NLP 系统恢复这种直觉的能力，我们引入了一项新任务，即推断个人叙述背后的寻求建议目标是什么。我们将其表述为完形填空测试，其目标是确定两个寻求建议问题中的哪一个被从给定的叙述中删除。构建此任务的主要挑战是为给定的叙述找到语义上合理的咨询问题对。为了应对这一挑战，我们设计了一种方法，利用人们在线分享的经验中的共性来自动提取适合完形填空任务的候选问题对。这产生了包含超过 20,000 个个人叙述的数据集，每个叙述都与一对相关的寻求建议的问题相匹配：一个实际上是叙述者的意图，另一个则不是。该数据集涵盖了非常广泛的人类经历，从约会到职业选择，再到被盗的 iPad。除了对叙述的语义理解之外，我们还使用人工注释来确定任务对常识和社会直觉的依赖程度。通过为这项新任务引入几个基线，我们证明了其可行性，并确定了更好地模拟叙述者意图的途径。
People often share personal narratives in order to seek advice from others. To properly infer the narrator’s intention, one needs to apply a certain degree of common sense and social intuition. To test the capabilities of NLP systems to recover such intuition, we introduce the new task of inferring what is the advice-seeking goal behind a personal narrative. We formulate this as a cloze test, where the goal is to identify which of two advice-seeking questions was removed from a given narrative. The main challenge in constructing this task is finding pairs of semantically plausible advice-seeking questions for given narratives. To address this challenge, we devise a method that exploits commonalities in experiences people share online to automatically extract pairs of questions that are appropriate candidates for the cloze task. This results in a dataset of over 20,000 personal narratives, each matched with a pair of related advice-seeking questions: one actually intended by the narrator, and the other one not. The dataset covers a very broad array of human experiences, from dating, to career options, to stolen iPads. We use human annotation to determine the degree to which the task relies on common sense and social intuition in addition to a semantic understanding of the narrative. By introducing several baselines for this new task we demonstrate its feasibility and identify avenues for better modeling the intention of the narrator.