@inproceedings{merkhofer-etal-2018-mitre,
    title = "{MITRE} at {S}em{E}val-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge",
    author = "Merkhofer, Elizabeth  and
      Henderson, John  and
      Bloom, David  and
      Strickhart, Laura  and
      Zarrella, Guido",
    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/S18-1181/",
    doi = "10.18653/v1/S18-1181",
    pages = "1078--1082",
    abstract = "This paper describes MITRE{'}s participation in SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. The techniques explored range from simple bag-of-ngrams classifiers to neural architectures with varied attention and alignment mechanisms. Logistic regression ties the systems together into an ensemble submitted for evaluation. The resulting system answers reading comprehension questions with 82.27{\%} accuracy."
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%0 Conference Proceedings
%T MITRE at SemEval-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge
%A Merkhofer, Elizabeth
%A Henderson, John
%A Bloom, David
%A Strickhart, Laura
%A Zarrella, Guido
%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 merkhofer-etal-2018-mitre
%X This paper describes MITRE’s participation in SemEval-2018 Task 11: Machine Comprehension using Commonsense Knowledge. The techniques explored range from simple bag-of-ngrams classifiers to neural architectures with varied attention and alignment mechanisms. Logistic regression ties the systems together into an ensemble submitted for evaluation. The resulting system answers reading comprehension questions with 82.27% accuracy.
%R 10.18653/v1/S18-1181
%U https://aclanthology.org/S18-1181/
%U https://doi.org/10.18653/v1/S18-1181
%P 1078-1082
Markdown (Informal)
[MITRE at SemEval-2018 Task 11: Commonsense Reasoning without Commonsense Knowledge](https://aclanthology.org/S18-1181/) (Merkhofer et al., SemEval 2018)
ACL