Papers › Transforming Question Answering Datasets Into Natural Language Inference Datasets

Transforming Question Answering Datasets Into Natural Language Inference Datasets

9 Sep 2018arXiv:1809.02922archive 2025-07-28

Dorottya Demszky, Kelvin Guu, Percy Liang

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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