  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.
