Transforming Question Answering Datasets Into Natural Language Inference Datasets

Dorottya Demszky; Kelvin Guu; Percy Liang

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arXiv · 1809.02922

Transforming Question Answering Datasets Into Natural Language Inference Datasets

Dorottya Demszky·Kelvin Guu·Percy Liang

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Abstract

Existing datasets for natural language inference (NLI) have propelled research on language understanding. We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. Despite being primarily trained on a single QA dataset, we show that it can be successfully applied to a variety of other QA resources. Using this system, we automatically derive a new freely available dataset of over 500k NLI examples (QA-NLI), and show that it exhibits a wide range of inference phenomena rarely seen in previous NLI datasets.

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Dorottya Demszky, Kelvin Guu, Percy Liang. 2018-09-09. Transforming Question Answering Datasets Into Natural Language Inference Datasets. https://arxiv.org/abs/1809.02922

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