# Transforming Question Answering Datasets Into Natural Language Inference Datasets Source: [Transforming Question Answering Datasets Into Natural Language Inference Datasets]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:11:58.696Z Authors: Dorottya Demszky, Kelvin Guu, Percy Liang Published: 2018-09-09T05:03:34.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()