%0 Conference Proceedings %T Adversarial NLI: A New Benchmark for Natural Language Understanding %A Nie, Yixin %A Williams, Adina %A Dinan, Emily %A Bansal, Mohit %A Weston, Jason %A Kiela, Douwe %Y Jurafsky, Dan %Y Chai, Joyce %Y Schluter, Natalie %Y Tetreault, Joel %S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics %D 2020 %8 July %I Association for Computational Linguistics %C Online %F nie-etal-2020-adversarial %X We introduce a new large-scale NLI benchmark dataset, collected via an iterative, adversarial human-and-model-in-the-loop procedure. We show that training models on this new dataset leads to state-of-the-art performance on a variety of popular NLI benchmarks, while posing a more difficult challenge with its new test set. Our analysis sheds light on the shortcomings of current state-of-the-art models, and shows that non-expert annotators are successful at finding their weaknesses. The data collection method can be applied in a never-ending learning scenario, becoming a moving target for NLU, rather than a static benchmark that will quickly saturate. %R 10.18653/v1/2020.acl-main.441 %U https://aclanthology.org/2020.acl-main.441 %U https://doi.org/10.18653/v1/2020.acl-main.441 %P 4885-4901