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    <abstract>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.</abstract>
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%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

Markdown (Informal)

[Adversarial NLI: A New Benchmark for Natural Language Understanding](https://aclanthology.org/2020.acl-main.441) (Nie et al., ACL 2020)

• Adversarial NLI: A New Benchmark for Natural Language Understanding (Nie et al., ACL 2020)

ACL

• Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020. Adversarial NLI: A New Benchmark for Natural Language Understanding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4885–4901, Online. Association for Computational Linguistics.