Adversarial NLI: A New Benchmark for Natural Language Understanding

作者：Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, J. Weston, Douwe Kiela · 发表于：Annual Meeting of the Association for Computational Linguistics · 年份：2019 · DOI：10.18653/v1/2020.acl-main.441 · 被引用次数：1259 · 研究领域：Computer Science

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.