Transforming Question Answering Datasets Into Natural Language Inference Datasets

TL;DR

This paper introduces a method to automatically convert large-scale question answering datasets into natural language inference datasets by learning a sentence transformation model, resulting in a new extensive dataset called QA-NLI.

Contribution

The authors present a novel approach to generate NLI datasets from QA data using a learned sentence transformation, enabling scalable and diverse NLI dataset creation.

Findings

Successfully derived over 500k NLI examples

QA-NLI exhibits diverse inference phenomena

Model generalizes across multiple QA datasets

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.