A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
TL
TL;DR
The Multi-Genre Natural Language Inference corpus is introduced, a dataset designed for use in the development and evaluation of machine learning models for sentence understanding and shows that it represents a substantially more difficult task than does the Stanford NLI corpus.
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. At 433k examples, this resource is one of the largest corpora available for natural language inference (a.k.a. recognizing textual entailment), improving upon available resources in both its coverage and difficulty. MultiNLI accomplishes this by offering data from ten distinct genres of written and spoken English, making it possible to evaluate systems on nearly the full complexity of the language, while supplying an explicit setting for evaluating cross-genre domain adaptation. In addition, an evaluation using existing machine learning models designed for the Stanford NLI corpus shows that it represents a substantially more difficult task than does that corpus, despite the two showing similar levels of inter-annotator agreement.
References56 items
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Living History
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The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
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Supervised Learning of Universal Sentence Representations from Natural Language Inference Data
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The sky is falling.
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