| # SWAG | |
| ### Paper | |
| Title: `SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference` | |
| Abstract: https://arxiv.org/pdf/1808.05326.pdf | |
| SWAG (Situations With Adversarial Generations) is an adversarial dataset | |
| that consists of 113k multiple choice questions about grounded situations. Each | |
| question is a video caption from LSMDC or ActivityNet Captions, with four answer | |
| choices about what might happen next in the scene. The correct answer is the | |
| (real) video caption for the next event in the video; the three incorrect | |
| answers are adversarially generated and human verified, so as to fool machines | |
| but not humans. | |
| Homepage: https://rowanzellers.com/swag/ | |
| ### Citation | |
| ``` | |
| @inproceedings{zellers2018swagaf, | |
| title={SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference}, | |
| author={Zellers, Rowan and Bisk, Yonatan and Schwartz, Roy and Choi, Yejin}, | |
| booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)", | |
| year={2018} | |
| } | |
| ``` | |
| ### Groups and Tasks | |
| #### Groups | |
| * Not a part of a task yet. | |
| #### Tasks | |
| * `swag` | |
| ### Checklist | |
| For adding novel benchmarks/datasets to the library: | |
| * [ ] Is the task an existing benchmark in the literature? | |
| * [ ] 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? | |
| * [ ] 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? | |