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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality ... Thrush, Tristan; Jiang, Ryan; Bartolo, Max. - : arXiv, 2022 BASE Show details 2 ANLIzing the Adversarial Natural Language Inference Dataset Williams, Adina; Thrush, Tristan; Kiela, Douwe In: Proceedings of the Society for Computation in Linguistics (2022) Abstract: We perform an in-depth error analysis of the Adversarial NLI (ANLI) dataset, a recently introduced large-scale human-and-model-in-the-loop natural language inference dataset collected dynamically over multiple rounds. We propose a fine-grained annotation scheme for the different aspects of inference responsible for the gold classification labels, and use it to hand-code the ANLI development sets in their entirety. We use these annotations to answer a variety of important questions: which models have the highest performance on each inference type, which inference types are most common, and which types are the most challenging for state-of-the-art models? We hope our annotations will enable more fine-grained evaluation of NLI models, and provide a deeper understanding of where models fail (and succeed). Both insights can guide us in training stronger models going forward. Keyword: annotation; artificial neural networks; Computational Linguistics; corpora; machine learning; natural language inference; natural language understanding; neural networks URL: https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=1250&context=scil https://scholarworks.umass.edu/scil/vol5/iss1/4 BASE Hide details

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