@inproceedings{hopkins-etal-2019-semeval,
title = "{S}em{E}val-2019 Task 10: Math Question Answering",
author = "Hopkins, Mark and
Le Bras, Ronan and
Petrescu-Prahova, Cristian and
Stanovsky, Gabriel and
Hajishirzi, Hannaneh and
Koncel-Kedziorski, Rik",
editor = "May, Jonathan and
Shutova, Ekaterina and
Herbelot, Aurelie and
Zhu, Xiaodan and
Apidianaki, Marianna and
Mohammad, Saif M.",
booktitle = "Proceedings of the 13th International Workshop on Semantic Evaluation",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/S19-2153/",
doi = "10.18653/v1/S19-2153",
pages = "893--899",
abstract = "We report on the SemEval 2019 task on math question answering. We provided a question set derived from Math SAT practice exams, including 2778 training questions and 1082 test questions. For a significant subset of these questions, we also provided SMT-LIB logical form annotations and an interpreter that could solve these logical forms. Systems were evaluated based on the percentage of correctly answered questions. The top system correctly answered 45\% of the test questions, a considerable improvement over the 17\% random guessing baseline."
}
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%0 Conference Proceedings
%T SemEval-2019 Task 10: Math Question Answering
%A Hopkins, Mark
%A Le Bras, Ronan
%A Petrescu-Prahova, Cristian
%A Stanovsky, Gabriel
%A Hajishirzi, Hannaneh
%A Koncel-Kedziorski, Rik
%Y May, Jonathan
%Y Shutova, Ekaterina
%Y Herbelot, Aurelie
%Y Zhu, Xiaodan
%Y Apidianaki, Marianna
%Y Mohammad, Saif M.
%S Proceedings of the 13th International Workshop on Semantic Evaluation
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F hopkins-etal-2019-semeval
%X We report on the SemEval 2019 task on math question answering. We provided a question set derived from Math SAT practice exams, including 2778 training questions and 1082 test questions. For a significant subset of these questions, we also provided SMT-LIB logical form annotations and an interpreter that could solve these logical forms. Systems were evaluated based on the percentage of correctly answered questions. The top system correctly answered 45% of the test questions, a considerable improvement over the 17% random guessing baseline.
%R 10.18653/v1/S19-2153
%U https://aclanthology.org/S19-2153/
%U https://doi.org/10.18653/v1/S19-2153
%P 893-899
Markdown (Informal)
[SemEval-2019 Task 10: Math Question Answering](https://aclanthology.org/S19-2153/) (Hopkins et al., SemEval 2019)
ACL
- Mark Hopkins, Ronan Le Bras, Cristian Petrescu-Prahova, Gabriel Stanovsky, Hannaneh Hajishirzi, and Rik Koncel-Kedziorski. 2019. SemEval-2019 Task 10: Math Question Answering. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 893–899, Minneapolis, Minnesota, USA. Association for Computational Linguistics.