![]() ![]() ![]() The fall is followed by the scene at the hospital, where the unfortunate man is being cared for by a team of physicians and nurses. People and boats are in the water attempting to save the man, while females are screaming all over the place. In a matter of seconds, the ocean for yards around had turned red with blood. He first hits the concrete face, then falls into the water. Because of the slide, he misses the water and instead lands on a concrete slab below where fishermen fish. The video shows the youth of a 16-year-old lad who jumps from Beirut's seafront promenade and slips before the plunge. The video is referred to by a description of the occurrence, such as Horrible Diving Accident, Bridge Fail, Worst Diving Accident, and Horrific Diving Accident.įurthermore, most people often call it an Awesome Diving Accident, Disgusting Diving Accident, Jump Accident, Cellphone Accident, and so on. It first surfaced on the Internet during the third week of July, but it has just recently begun to become viral in a big way, around the second week of September. Thanks to for adding this dataset.A video of a man with a split face after hitting the cement surface while diving has gone viral on Reddit. An example looks like this: ,īooktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing (EMNLP)", The regular configuration should be used for modeling. The dataset introduces the task of grounded commonsense inference, unifying natural language inference and commonsense reasoning. SWAG aims to be a benchmark forĮvaluating grounded commonsense NLI and for learning representations. The three incorrect answers are adversarially generated and human verified, The correct answer is the (real) video caption for the next event in the video With four answer choices about what might happen next in the scene. ![]() (73k training, 20k validation, 20k test).Įach question is a video caption from LSMDC or ActivityNet Captions, The dataset consists of 113k multiple choice questions about grounded situations Inference, unifying natural language inference and physically grounded reasoning. Is a large-scale dataset for this task of grounded commonsense SWAG (Situations With Adversarial Generations) ![]() Humans can reason about the situation and anticipate what might come Given a partial description like "she opened the hood of the car," Dataset Card for Situations With Adversarial Generations ![]()
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