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QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

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arxiv 1909.03553 v1 pith:KABUQGRI submitted 2019-09-08 cs.CL cs.AI

classification cs.CLcs.AI
keywords qualitativequartztextualdatasetgeneralknowledgeopen-domainquestions
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We introduce the first open-domain dataset, called QuaRTz, for reasoning about textual qualitative relationships. QuaRTz contains general qualitative statements, e.g., "A sunscreen with a higher SPF protects the skin longer.", twinned with 3864 crowdsourced situated questions, e.g., "Billy is wearing sunscreen with a lower SPF than Lucy. Who will be best protected from the sun?", plus annotations of the properties being compared. Unlike previous datasets, the general knowledge is textual and not tied to a fixed set of relationships, and tests a system's ability to comprehend and apply textual qualitative knowledge in a novel setting. We find state-of-the-art results are substantially (20%) below human performance, presenting an open challenge to the NLP community.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text

    cs.CL 2025-06 conditional novelty 6.0 of 10

    A new 8TB openly-licensed text corpus trains 7B LLMs that are competitive with Llama 1/2, showing that performant models need not depend on unlicensed web data.

  2. An Interdisciplinary Review of Commonsense Reasoning and Intent Detection

    cs.CL 2025-06 reject novelty 3.0 of 10

    A survey that organizes 28 (actually 27) papers on commonsense reasoning and intent detection into eight themes, but with factual misattributions and inconsistent venue selection.

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