Given a partial description like "she opened the hood of the car," humans can
reason about the situation and anticipate what might come next ("then, she
examined the engine"). In this paper, we introduce the task of grounded
commonsense inference, unifying natural language inference and commonsense
reasoning.
We present SWAG, a new dataset with 113k multiple choice questions about a
rich spectrum of grounded situations. To address the recurring challenges of
the annotation artifacts and human biases found in many existing datasets, we
propose Adversarial Filtering (AF), a novel procedure that constructs a
de-biased dataset by iteratively training an ensemble of stylistic classifiers,
and using them to filter the data. To account for the aggressive adversarial
filtering, we use state-of-the-art language models to massively oversample a
diverse set of potential counterfactuals. Empirical results demonstrate that
while humans can solve the resulting inference problems with high accuracy
(88%), various competitive models struggle on our task. We provide
comprehensive analysis that indicates significant opportunities for future
research.

本文提出了基于语言理解和常识推理任务的通用语境推断任务。为解决当前数据集进行注释所遇到的人为误差和注释偏见问题，使用对抗训练技术提出了 Adversarial Filtering 方法，并针对过度过滤的问题使用最先进的语言模型来大规模扩增不同的对抗情境。实验结果表明人可以高效地解决我们的任务，而其它竞争模型则表现不佳，未来仍存在大量的研究机会。