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Please use this identifier to cite or link to this item: http://hdl.handle.net/2451/41728
Title: The SNLI Corpus
Authors: Bowman, Samuel R.
Angeli, Gabor
Potts, Christopher
Manning, Christopher D.
Issue Date: Jun-2015
Citation: Samuel R. Bowman, Gabor Angeli, Christopher Potts, and Christopher D. Manning. 2015. A large annotated corpus for learning natural language inference. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP).
Abstract: The SNLI corpus (version 1.0) is a collection of 570k human-written English sentence pairs manually labeled for balanced classification with the labels entailment, contradiction, and neutral, supporting the task of natural language inference (NLI), also known as recognizing textual entailment (RTE). We aim for it to serve both as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, as well as a resource for developing NLP models of any kind.
URI: http://hdl.handle.net/2451/41728
metadata.dc.rights: Dataset licensed as CC-BY-SA 4.0. Paper licensed as CC-BY-NC-SA 3.0
Appears in Collections:Machine Learning for Language Lab

Files in This Item:
File Description SizeFormat 
snli_1.0.zipSNLI 1.0 (data, zip)92.33 MBUnknownView/Open
snli_paper.pdfSNLI description paper (PDF)248.05 kBAdobe PDFView/Open


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