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Hits 1 – 2 of 2
1
Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality ...
Thrush, Tristan
;
Jiang, Ryan
;
Bartolo, Max
. - : arXiv, 2022
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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
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