@inproceedings{SemEval2018Task11,
title = {SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge},
author = {Michael Roth and Stefan Thater andSimon Ostermann and Ashutosh Modi and Manfred Pinkal},
url = {https://aclanthology.org/S18-1119},
doi = {https://doi.org/10.18653/v1/S18-1119},
year = {2018},
date = {2018},
booktitle = {Proceedings of the 12th International Workshop on Semantic Evaluation},
pages = {747-757},
publisher = {Association for Computational Linguistics},
address = {New Orleans, Louisiana},
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%.},
pubstate = {published},
type = {inproceedings}
}