A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
This provides a broad-coverage benchmark for natural language inference research, though it is incremental as it builds on existing NLI datasets.
The authors introduced the Multi-Genre Natural Language Inference (MultiNLI) corpus, a dataset with 433k examples covering ten genres of English, to develop and evaluate machine learning models for sentence understanding and cross-genre domain adaptation.
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.