WIQA: A dataset for “What if...” reasoning over procedural text

Conference on Empirical Methods in Natural Language Processing 2019 113 cites
2019:62020:92021:122022:152023:172024:182025:192026:17
2019 2026
113

Niket Tandon, Bhavana Dalvi, Keisuke Sakaguchi, Antoine Bosselut, Peter Clark

Abstract

We introduce WIQA, the first large-scale dataset of “What if...” questions over procedural text. WIQA contains a collection of paragraphs, each annotated with multiple influence graphs describing how one change affects another, and a large (40k) collection of “What if...?” multiple-choice questions derived from these. For example, given a paragraph about beach erosion, would stormy weather hasten or decelerate erosion? 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.

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