  Given questions regarding some prototypical situation such as Name something
that people usually do before they leave the house for work? a human can easily
answer them via acquired experiences. There can be multiple right answers for
such questions, with some more common for a situation than others. This paper
introduces a new question answering dataset for training and evaluating common
sense reasoning capabilities of artificial intelligence systems in such
prototypical situations. The training set is gathered from an existing set of
questions played in a long-running international game show FAMILY- FEUD. The
hidden evaluation set is created by gathering answers for each question from
100 crowd-workers. We also propose a generative evaluation task where a model
has to output a ranked list of answers, ideally covering all prototypical
answers for a question. After presenting multiple competitive baseline models,
we find that human performance still exceeds model scores on all evaluation
metrics with a meaningful gap, supporting the challenging nature of the task.
