We introduce WIQA, the first large-scale dataset of "What if..." questions
over procedural text. WIQA contains three parts: a collection of paragraphs
each describing a process, e.g., beach erosion; a set of crowdsourced influence
graphs for each paragraph, describing how one change affects another; and a
large (40k) collection of "What if...?" multiple-choice questions derived from
the graphs. For example, given a paragraph about beach erosion, would stormy
weather result in more or less erosion (or have no effect)? The task is to
answer the questions, given their associated paragraph. WIQA contains three
kinds of questions: perturbations to steps mentioned in the paragraph; external
(out-of-paragraph) perturbations requiring commonsense knowledge; and
irrelevant (no effect) perturbations. We find that state-of-the-art models
achieve 73.8% accuracy, well below the human performance of 96.3%. We analyze
the challenges, in particular tracking chains of influences, and present the
dataset as an open challenge to the community.

WIQA 是一个包含大量关于程序性文本 “What if…” 问题的数据集，其中包括描述过程的段落、描述一个变化如何影响另一个的众包影响图和从图中派生的大量 “what if…” 多项选择问题，包括对段落中提到的步骤进行干扰、需要常识知识的外部干扰以及无关紧要的干扰等三种类型。我们发现，当前的最先进模型的准确率为 73.8％，远低于人类表现的 96.3%。该数据集是社区的一个开放性挑战，其中一个主要的挑战是跟踪影响链。