README.md

metadata

annotations_creators:
  - crowdsourced
language:
  - en
language_creators:
  - crowdsourced
  - expert-generated
license:
  - apache-2.0
multilinguality:
  - monolingual
pretty_name: MathQA
size_categories:
  - 10K<n<100K
source_datasets:
  - extended|aqua_rat
task_categories:
  - question-answering
task_ids:
  - multiple-choice-qa
paperswithcode_id: mathqa
dataset_info:
  features:
    - name: Problem
      dtype: string
    - name: Rationale
      dtype: string
    - name: options
      dtype: string
    - name: correct
      dtype: string
    - name: annotated_formula
      dtype: string
    - name: linear_formula
      dtype: string
    - name: category
      dtype: string
  splits:
    - name: test
      num_bytes: 1844184
      num_examples: 2985
    - name: train
      num_bytes: 18368826
      num_examples: 29837
    - name: validation
      num_bytes: 2752969
      num_examples: 4475
  download_size: 7302821
  dataset_size: 22965979

Dataset Card for MathQA

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks and Leaderboards
  • Languages
• Dataset Structure
  • Data Instances
  • Data Fields
  • Data Splits
• Dataset Creation
  • Curation Rationale
  • Source Data
  • Annotations
  • Personal and Sensitive Information
• Considerations for Using the Data
  • Social Impact of Dataset
  • Discussion of Biases
  • Other Known Limitations
• Additional Information
  • Dataset Curators
  • Licensing Information
  • Citation Information
  • Contributions

Dataset Description

• Homepage: https://math-qa.github.io/math-QA/
• Repository: More Information Needed
• Paper: MathQA: Towards Interpretable Math Word Problem Solving with Operation-Based Formalisms
• Point of Contact: More Information Needed
• Size of downloaded dataset files: 7.30 MB
• Size of the generated dataset: 22.96 MB
• Total amount of disk used: 30.27 MB

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

More Information Needed

Languages

More Information Needed

Dataset Structure

Data Instances

default

• 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.

default

• 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

More Information Needed

Who are the source language producers?

More Information Needed

Annotations

Annotation process

More Information Needed

Who are the annotators?

More Information Needed

Personal and Sensitive Information

More Information Needed

Considerations for Using the Data

Social Impact of Dataset

More Information Needed

Discussion of Biases

More Information Needed

Other Known Limitations

More Information Needed

Additional Information

Dataset Curators

More Information Needed

Licensing Information

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

Citation Information

@inproceedings{amini-etal-2019-mathqa,
    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 = "https://aclanthology.org/N19-1245",
    doi = "10.18653/v1/N19-1245",
    pages = "2357--2367",
}

Contributions

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