@InProceedings{ostermann-EtAl:2018:S18-1,
  author    = {Ostermann, Simon  and  Roth, Michael  and  Modi, Ashutosh  and  Thater, Stefan  and  Pinkal, Manfred},
  title     = {SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge},
  booktitle = {Proceedings of The 12th International Workshop on Semantic Evaluation},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {747--757},
  abstract  = {This report summarizes the results of the SemEval 2018 task on machine comprehension using commonsense knowledge. For this machine comprehension task, we created a new corpus, MCScript. It contains a high number of questions that require commonsense knowledge for finding the correct answer. 11 teams from 4 different countries participated in this shared task, most of them used neural approaches. The best performing system achieves an accuracy of 83.95%, outperforming the baselines by a large margin, but still far from the human upper bound, which was found to be at 98%.},
  url       = {http://www.aclweb.org/anthology/S18-1119}
}