Analyzing Dynamic Adversarial Training Data in the Limit

@inproceedings{Wallace2021AnalyzingDA,
  title={Analyzing Dynamic Adversarial Training Data in the Limit},
  author={Eric Wallace and Adina Williams and Robin Jia and Douwe Kiela},
  booktitle={Findings},
  year={2021}
}
To create models that are robust across a wide range of test inputs, training datasets should include diverse examples that span numerous phenomena. Dynamic adversarial data collection (DADC), where annotators craft examples that challenge continually improving models, holds promise as an approach for generating such diverse training sets. Prior work has shown that running DADC over 1-3 rounds can help models fix some error types, but it does not necessarily lead to better generalization beyond… 

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