# A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

## TL;DR

The paper presents the MultiNLI corpus, a large, diverse dataset for natural language inference that enables comprehensive evaluation and domain adaptation of sentence understanding models.

## Contribution

It introduces a broad-coverage, multi-genre NLI dataset with 433,000 examples, enhancing evaluation of models across varied language genres.

## Key findings

- Largest NLI corpus with 433k examples
- Includes ten distinct genres for comprehensive testing
- Facilitates evaluation of cross-genre domain adaptation

## Abstract

This paper introduces the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding. In addition to being one of the largest corpora available for the task of NLI, at 433k examples, this corpus improves upon available resources in its coverage: it offers data from ten distinct genres of written and spoken English--making it possible to evaluate systems on nearly the full complexity of the language--and it offers an explicit setting for the evaluation of cross-genre domain adaptation.