@inproceedings{luo-etal-2019-problemsolver,
title = "{P}roblem{S}olver at {S}em{E}val-2019 Task 10: Sequence-to-Sequence Learning and Expression Trees",
author = "Luo, Xuefeng and
Baranova, Alina and
Biegert, Jonas",
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/S19-2227",
doi = "10.18653/v1/S19-2227",
pages = "1292--1296",
abstract = "This paper describes our participation in SemEval-2019 shared task {``}Math Question Answering{''}, where the aim is to create a program that could solve the Math SAT questions automatically as accurately as possible. We went with a dual-pronged approach, building a Sequence-to-Sequence Neural Network pre-trained with augmented data that could answer all categories of questions and a Tree system, which can only answer a certain type of questions. The systems did not perform well on the entire test data given in the task, but did decently on the questions they were actually capable of answering. The Sequence-to-Sequence Neural Network model managed to get slightly better than our baseline of guessing {``}A{''} for every question, while the Tree system additionally improved the results.",
}
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<abstract>This paper describes our participation in SemEval-2019 shared task “Math Question Answering”, where the aim is to create a program that could solve the Math SAT questions automatically as accurately as possible. We went with a dual-pronged approach, building a Sequence-to-Sequence Neural Network pre-trained with augmented data that could answer all categories of questions and a Tree system, which can only answer a certain type of questions. The systems did not perform well on the entire test data given in the task, but did decently on the questions they were actually capable of answering. The Sequence-to-Sequence Neural Network model managed to get slightly better than our baseline of guessing “A” for every question, while the Tree system additionally improved the results.</abstract>
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%0 Conference Proceedings
%T ProblemSolver at SemEval-2019 Task 10: Sequence-to-Sequence Learning and Expression Trees
%A Luo, Xuefeng
%A Baranova, Alina
%A Biegert, Jonas
%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 luo-etal-2019-problemsolver
%X This paper describes our participation in SemEval-2019 shared task “Math Question Answering”, where the aim is to create a program that could solve the Math SAT questions automatically as accurately as possible. We went with a dual-pronged approach, building a Sequence-to-Sequence Neural Network pre-trained with augmented data that could answer all categories of questions and a Tree system, which can only answer a certain type of questions. The systems did not perform well on the entire test data given in the task, but did decently on the questions they were actually capable of answering. The Sequence-to-Sequence Neural Network model managed to get slightly better than our baseline of guessing “A” for every question, while the Tree system additionally improved the results.
%R 10.18653/v1/S19-2227
%U https://aclanthology.org/S19-2227
%U https://doi.org/10.18653/v1/S19-2227
%P 1292-1296
Markdown (Informal)
[ProblemSolver at SemEval-2019 Task 10: Sequence-to-Sequence Learning and Expression Trees](https://aclanthology.org/S19-2227) (Luo et al., SemEval 2019)
ACL