MAWPS: A Math Word Problem Repository

Koncel-Kedziorski, Rik; Roy, Subhro; Amini, Aida; Kushman, Nate; Hajishirzi, Hannaneh

doi:10.18653/v1/n16-1136

Cited by 147 publications

(106 citation statements)

References 14 publications

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“…Additional Datasets Several smaller datasets have been compiled in recent years. Most of these works have focused on algebra word problems, including MaWPS (Koncel-Kedziorski et al, 2016), Alg514 (Kushman et al, 2014), and DRAW-1K (Upadhyay and Chang, 2017). Many of these datasets have sought to align underlying equations or systems of equations with word problem text.…”

Section: Background and Related Workmentioning

confidence: 99%

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Amini¹,

Gabriel

Lin

et al. 2019

Proceedings of the 2019 Conference of the North

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We introduce a large-scale dataset of math word problems and an interpretable neural math problem solver that learns to map problems to operation programs. Due to annotation challenges, current datasets in this domain have been either relatively small in scale or did not offer precise operational annotations over diverse problem types. We introduce a new representation language to model precise operation programs corresponding to each math problem that aim to improve both the performance and the interpretability of the learned models. Using this representation language, our new dataset, MathQA, significantly enhances the AQuA dataset with fully-specified operational programs. We additionally introduce a neural sequence-to-program model enhanced with automatic problem categorization. Our experiments show improvements over competitive baselines in our MathQA as well as the AQuA datasets. The results are still significantly lower than human performance indicating that the dataset poses new challenges for future research. Our dataset is available at: https: //math-qa.github.io/math-QA/.

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Section: Background and Related Workmentioning

confidence: 99%

Untitled

Amini¹,

Gabriel

Lin

et al. 2019

Proceedings of the 2019 Conference of the North

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“…Finally, we evaluate the systems on the Aggregate dataset. Following previous work (Roy and Roth, 2017), we compute two subsets of Aggregate comprising 756 problems each, using the MAWPS (Koncel-Kedziorski et al, 2016) Table 4 also shows that the other systems do not learn the right abstraction, even when trained on Aggregate.…”

Section: Results On the New Datasetmentioning

confidence: 99%

Mapping to Declarative Knowledge for Word Problem Solving

Roy

Roth

2018

TACL

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Math word problems form a natural abstraction to a range of quantitative reasoning problems, such as understanding financial news, sports results, and casualties of war. Solving such problems requires the understanding of several mathematical concepts such as dimensional analysis, subset relationships, etc. In this paper, we develop declarative rules which govern the translation of natural language description of these concepts to math expressions. We then present a framework for incorporating such declarative knowledge into word problem solving. Our method learns to map arithmetic word problem text to math expressions, by learning to select the relevant declarative knowledge for each operation of the solution expression. This provides a way to handle multiple concepts in the same problem while, at the same time, support interpretability of the answer expression. Our method models the mapping to declarative knowledge as a latent variable, thus removing the need for expensive annotations. Experimental evaluation suggests that our domain knowledge based solver outperforms all other systems, and that it generalizes better in the realistic case where the training data it is exposed to is biased in a different way than the test data.

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“…To train their systems, participants were permitted to use the following public resources: (a) the provided SAT training data and annotations, (b) data collected in MAWPS (Koncel-Kedziorski et al, 2016), (c) AQuA. Participants were also welcome to use standard public corpora for training word vector representations, language models, etc.…”

Section: Evaluation Methodology and Baselinementioning

confidence: 99%

SemEval-2019 Task 10: Math Question Answering

Hopkins

Bras²,

Petrescu-Prahova³

et al. 2019

Proceedings of the 13th International Workshop on Semantic Evaluation

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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. 1 Overview Over the past four years, there has been a surge of interest in math question answering. Research groups from around the globe have published papers on the topic, including MIT (

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MAWPS: A Math Word Problem Repository

Cited by 147 publications

References 14 publications

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Mapping to Declarative Knowledge for Word Problem Solving

SemEval-2019 Task 10: Math Question Answering

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