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A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference
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.Expand
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SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems
In the last year, new models and methods for pretraining and transfer learning have driven striking performance improvements across a range of language understanding tasks. The GLUE benchmark,Expand
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  • Open Access
The RepEval 2017 Shared Task: Multi-Genre Natural Language Inference with Sentence Representations
This paper presents the results of the RepEval 2017 Shared Task, which evaluated neural network sentence representation learning models on the Multi-Genre Natural Language Inference corpus (MultiNLI)Expand
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  • Open Access
ListOps: A Diagnostic Dataset for Latent Tree Learning
Latent tree learning models learn to parse a sentence without syntactic supervision, and use that parse to build the sentence representation. Existing work on such models has shown that, while theyExpand
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Human vs. Muppet: A Conservative Estimate of Human Performance on the GLUE Benchmark
The GLUE benchmark (Wang et al., 2019b) is a suite of language understanding tasks which has seen dramatic progress in the past year, with average performance moving from 70.0 at launch to 83.9,Expand
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  • Open Access
Natural Language Understanding with the Quora Question Pairs Dataset
This paper explores the task Natural Language Understanding (NLU) by looking at duplicate question detection in the Quora dataset. We conducted extensive exploration of the dataset and used variousExpand
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  • Open Access
CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models
Warning: This paper contains explicit statements of offensive stereotypes and may be upsetting. Pretrained language models, especially masked language models (MLMs) have seen success across many NLPExpand
Latent Structure Models for Natural Language Processing
Latent structure models are a powerful tool for modeling compositional data, discovering linguistic structure, and building NLP pipelines (Smith, 2011). Words, sentences, paragraphs, and documentsExpand
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  • Open Access