Adversarial NLI: A New Benchmark for Natural Language Understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, Douwe Kiela
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.- Anthology ID:
- 2020.acl-main.441
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4885–4901
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.441
- DOI:
- 10.18653/v1/2020.acl-main.441
- Cite (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.
- Cite (Informal):
- Adversarial NLI: A New Benchmark for Natural Language Understanding (Nie et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2020.acl-main.441.pdf
- Code
- facebookresearch/anli + additional community code
- Data
- ANLI, FEVER, GLUE, HotpotQA, ImageNet, MultiNLI, SNLI, SQuAD, SWAG, Visual Question Answering