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1
Winoground: Probing Vision and Language Models for Visio-Linguistic Compositionality ...
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2
ANLIzing the Adversarial Natural Language Inference Dataset
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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3
Investigating Novel Verb Learning in BERT: Selectional Preference Classes and Alternation-Based Syntactic Generalization
In: Association for Computational Linguistics (2021)
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4
Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection ...
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5
Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation ...
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6
Compositional Neural Machine Translation by Removing the Lexicon from Syntax ...
Thrush, Tristan. - : arXiv, 2020
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7
SAL : a Self-Aware Learning system ; Self-Aware Learning system
Thrush, Tristan Andrew Fraser.. - : Massachusetts Institute of Technology, 2019
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