@inproceedings{ostermann-etal-2018-semeval,
title = "{S}em{E}val-2018 Task 11: Machine Comprehension Using Commonsense Knowledge",
author = "Ostermann, Simon and
Roth, Michael and
Modi, Ashutosh and
Thater, Stefan and
Pinkal, Manfred",
editor = "Apidianaki, Marianna and
Mohammad, Saif M. and
May, Jonathan and
Shutova, Ekaterina and
Bethard, Steven and
Carpuat, Marine",
booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S18-1119",
doi = "10.18653/v1/S18-1119",
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{\%}.",
}
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<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%.</abstract>
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%0 Conference Proceedings
%T SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge
%A Ostermann, Simon
%A Roth, Michael
%A Modi, Ashutosh
%A Thater, Stefan
%A Pinkal, Manfred
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Bethard, Steven
%Y Carpuat, Marine
%S Proceedings of the 12th International Workshop on Semantic Evaluation
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F ostermann-etal-2018-semeval
%X 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%.
%R 10.18653/v1/S18-1119
%U https://aclanthology.org/S18-1119
%U https://doi.org/10.18653/v1/S18-1119
%P 747-757
Markdown (Informal)
[SemEval-2018 Task 11: Machine Comprehension Using Commonsense Knowledge](https://aclanthology.org/S18-1119) (Ostermann et al., SemEval 2018)
ACL