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 trivia game show -- Family Feud . The hidden evaluation set is created by gathering answers for each question from 100 crowd-workers. We also propose an open-domain task where a model has to output a ranked list of answers, ideally covering all prototypical answers for a question. On evaluating our dataset with various competitive state-of-the-art models, we find there is a significant gap between the best model and human performance on a number of evaluation metrics .
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 .
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common-sense
common sense
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trivia game show -- Family Feud
game show FAMILY- FEUD
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an open-domain
a generative evaluation
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On evaluating our dataset with various competitive state-of-the-art
After presenting multiple competitive baseline
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there is a significant gap between the best model and human performance on a number of evaluation metrics
that human performance still exceeds model scores on all evaluation metrics with a meaningful gap, supporting the challenging nature of the task