| # ANLI | |
| ### Paper | |
| Title: `Adversarial NLI: A New Benchmark for Natural Language Understanding` | |
| Paper Link: https://arxiv.org/abs/1910.14599 | |
| Adversarial NLI (ANLI) is a dataset collected via an iterative, adversarial | |
| human-and-model-in-the-loop procedure. It consists of three rounds that progressively | |
| increase in difficulty and complexity, and each question-answer includes annotator- | |
| provided explanations. | |
| Homepage: https://github.com/facebookresearch/anli | |
| ### Citation | |
| ``` | |
| @inproceedings{nie-etal-2020-adversarial, | |
| title = "Adversarial {NLI}: A New Benchmark for Natural Language Understanding", | |
| author = "Nie, Yixin and | |
| Williams, Adina and | |
| Dinan, Emily and | |
| Bansal, Mohit and | |
| Weston, Jason and | |
| Kiela, Douwe", | |
| booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics", | |
| year = "2020", | |
| publisher = "Association for Computational Linguistics", | |
| } | |
| ``` | |
| ### Groups and Tasks | |
| #### Groups | |
| * `anli`: Evaluates `anli_r1`, `anli_r2`, and `anli_r3` | |
| #### Tasks | |
| * `anli_r1`: The data collected adversarially in the first round. | |
| * `anli_r2`: The data collected adversarially in the second round, after training on the previous round's data. | |
| * `anli_r3`: The data collected adversarially in the third round, after training on the previous multiple rounds of data. | |
| ### Checklist | |
| For adding novel benchmarks/datasets to the library: | |
| * [x] Is the task an existing benchmark in the literature? | |
| * [x] Have you referenced the original paper that introduced the task? | |
| * [ ] If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test? | |
| If other tasks on this dataset are already supported: | |
| * [ ] Is the "Main" variant of this task clearly denoted? | |
| * [x] Have you provided a short sentence in a README on what each new variant adds / evaluates? | |
| * [ ] Have you noted which, if any, published evaluation setups are matched by this variant? | |