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Dataset Card for MathQA

Dataset Summary

We introduce a large-scale dataset of math word problems.

Our dataset is gathered by using a new representation language to annotate over the AQuA-RAT dataset with fully-specified operational programs.

AQuA-RAT has provided the questions, options, rationale, and the correct options.

Supported Tasks and Leaderboards

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Dataset Structure

Data Instances


  • Size of downloaded dataset files: 7.30 MB
  • Size of the generated dataset: 22.96 MB
  • Total amount of disk used: 30.27 MB

An example of 'train' looks as follows.

    "Problem": "a multiple choice test consists of 4 questions , and each question has 5 answer choices . in how many r ways can the test be completed if every question is unanswered ?",
    "Rationale": "\"5 choices for each of the 4 questions , thus total r of 5 * 5 * 5 * 5 = 5 ^ 4 = 625 ways to answer all of them . answer : c .\"",
    "annotated_formula": "power(5, 4)",
    "category": "general",
    "correct": "c",
    "linear_formula": "power(n1,n0)|",
    "options": "a ) 24 , b ) 120 , c ) 625 , d ) 720 , e ) 1024"

Data Fields

The data fields are the same among all splits.


  • Problem: a string feature.
  • Rationale: a string feature.
  • options: a string feature.
  • correct: a string feature.
  • annotated_formula: a string feature.
  • linear_formula: a string feature.
  • category: a string feature.

Data Splits

name train validation test
default 29837 4475 2985

Dataset Creation

Curation Rationale

More Information Needed

Source Data

Initial Data Collection and Normalization

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Who are the source language producers?

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Annotation process

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Who are the annotators?

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Personal and Sensitive Information

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Considerations for Using the Data

Social Impact of Dataset

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Discussion of Biases

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Other Known Limitations

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Additional Information

Dataset Curators

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Licensing Information

The dataset is licensed under the Apache License, Version 2.0.

Citation Information

    title = "{M}ath{QA}: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms",
    author = "Amini, Aida  and
      Gabriel, Saadia  and
      Lin, Shanchuan  and
      Koncel-Kedziorski, Rik  and
      Choi, Yejin  and
      Hajishirzi, Hannaneh",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "",
    doi = "10.18653/v1/N19-1245",
    pages = "2357--2367",


Thanks to @thomwolf, @lewtun, @patrickvonplaten for adding this dataset.

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