We introduce a large dataset of narrative texts and questions about these texts, intended to be used in a machine comprehension task that requires reasoning using commonsense knowledge. Our dataset complements similar datasets in that we focus on stories about everyday activities, such as going to the movies or working in the garden, and that the questions require commonsense knowledge, or more specifically, script knowledge, to be answered. We show that our mode of data collection via crowdsourcing results in a substantial amount of such inference questions. The dataset forms the basis of a shared task on commonsense and script knowledge organized at SemEval 2018 and provides challenging test cases for the broader natural language understanding community.
Two notable exceptions are the and datasets. @cite_2 is a dataset of newswire texts from CNN with questions and answers written by crowdsourcing workers. closely resembles our own data collection with respect to the method of data acquisition. As for our data collection, full texts were not shown to workers as a basis for question formulation, but only the text's title and a short summary, to avoid literal repetitions and support the generation of non-trivial questions requiring background knowledge. The NewsQA text collection differs from ours in domain and genre (newswire texts vs. narrative stories about everyday events). Knowledge required to answer the questions is mostly factual knowledge and script knowledge is only marginally relevant. Also, the task is not exactly question answering, but identification of document passages containing the answer.
@cite_2
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We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. We collect this dataset through a four-stage process designed to solicit exploratory questions that require reasoning. A thorough analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (0.198 in F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available at this https URL.