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A large annotated corpus for learning natural language inference
Understanding entailment and contradiction is fundamental to understanding natural language, and inference about entailment and contradiction is a valuable testing ground for the development ofExpand
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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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Generating Sentences from a Continuous Space
The standard recurrent neural network language model (RNNLM) generates sentences one word at a time and does not work from an explicit global sentence representation. In this work, we introduce andExpand
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GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding
For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way thatExpand
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XNLI: Evaluating Cross-lingual Sentence Representations
State-of-the-art natural language processing systems rely on supervision in the form of annotated data to learn competent models. These models are generally trained on data in a single languageExpand
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Annotation Artifacts in Natural Language Inference Data
Large-scale datasets for natural language inference are created by presenting crowd workers with a sentence (premise), and asking them to generate three new sentences (hypotheses) that it entails,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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A Fast Unified Model for Parsing and Sentence Understanding
Tree-structured neural networks exploit valuable syntactic parse information as they interpret the meanings of sentences. However, they suer from two key technical problems that make them slow andExpand
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Neural Network Acceptability Judgments
This paper investigates the ability of artificial neural networks to judge the grammatical acceptability of a sentence, with the goal of testing their linguistic competence. We introduce the CorpusExpand
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A Gold Standard Dependency Corpus for English
We present a gold standard annotation of syntactic dependencies in the English Web Treebank corpus using the Stanford Dependencies standard. This resource addresses the lack of a gold standardExpand
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