  Natural language inference (NLI) is formulated as a unified framework for
solving various NLP problems such as relation extraction, question answering,
summarization, etc. It has been studied intensively in the past few years
thanks to the availability of large-scale labeled datasets. However, most
existing studies focus on merely sentence-level inference, which limits the
scope of NLI's application in downstream NLP problems. This work presents
DocNLI -- a newly-constructed large-scale dataset for document-level NLI.
DocNLI is transformed from a broad range of NLP problems and covers multiple
genres of text. The premises always stay in the document granularity, whereas
the hypotheses vary in length from single sentences to passages with hundreds
of words. Additionally, DocNLI has pretty limited artifacts which unfortunately
widely exist in some popular sentence-level NLI datasets. Our experiments
demonstrate that, even without fine-tuning, a model pretrained on DocNLI shows
promising performance on popular sentence-level benchmarks, and generalizes
well to out-of-domain NLP tasks that rely on inference at document granularity.
Task-specific fine-tuning can bring further improvements. Data, code, and
pretrained models can be found at https://github.com/salesforce/DocNLI.
