Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

• URL: http://arxiv.org/abs/2101.02235v1
• Date: Wed, 6 Jan 2021 19:14:23 GMT
• Title: Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
• Authors: Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, Jonathan Berant
• Abstract summary: StrategyQA is a benchmark where the required reasoning steps are implicit in the question, and should be inferred using a strategy. We propose a data collection procedure that combines term-based priming to inspire annotators, careful control over the annotator population, and adversarial filtering for eliminating reasoning shortcuts. Overall, StrategyQA includes 2,780 examples, each consisting of a strategy question, its decomposition, and evidence paragraphs.
• Score: 78.68534915690404
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: A key limitation in current datasets for multi-hop reasoning is that the required steps for answering the question are mentioned in it explicitly. In this work, we introduce StrategyQA, a question answering (QA) benchmark where the required reasoning steps are implicit in the question, and should be inferred using a strategy. A fundamental challenge in this setup is how to elicit such creative questions from crowdsourcing workers, while covering a broad range of potential strategies. We propose a data collection procedure that combines term-based priming to inspire annotators, careful control over the annotator population, and adversarial filtering for eliminating reasoning shortcuts. Moreover, we annotate each question with (1) a decomposition into reasoning steps for answering it, and (2) Wikipedia paragraphs that contain the answers to each step. Overall, StrategyQA includes 2,780 examples, each consisting of a strategy question, its decomposition, and evidence paragraphs. Analysis shows that questions in StrategyQA are short, topic-diverse, and cover a wide range of strategies. Empirically, we show that humans perform well (87%) on this task, while our best baseline reaches an accuracy of $\sim$66%.

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