# Dataset: aqua_rat

Languages: en
Multilinguality: monolingual
Size Categories: 10K<n<100K
Language Creators: crowdsourcedexpert-generated
Annotations Creators: crowdsourced
Source Datasets: original

# Dataset Card for AQUA-RAT

### Dataset Summary

A large-scale dataset consisting of approximately 100,000 algebraic word problems. The solution to each question is explained step-by-step using natural language. This data is used to train a program generation model that learns to generate the explanation, while generating the program that solves the question.

en

## Dataset Structure

### Data Instances

{
"question": "A grocery sells a bag of ice for \$1.25, and makes 20% profit. If it sells 500 bags of ice, how much total profit does it make?",
"options": ["A)125", "B)150", "C)225", "D)250", "E)275"],
"rationale": "Profit per bag = 1.25 * 0.20 = 0.25\nTotal profit = 500 * 0.25 = 125\nAnswer is A.",
"correct": "A"
}

### Data Fields

• question : (str) A natural language definition of the problem to solve
• options : (list(str)) 5 possible options (A, B, C, D and E), among which one is correct
• rationale : (str) A natural language description of the solution to the problem
• correct : (str) The correct option

### Data Splits

Train Valid Test
Examples 97467 254 254

## Considerations for Using the Data

### Licensing Information

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

### Citation Information

@article{ling2017program,
title={Program induction by rationale generation: Learning to solve and explain algebraic word problems},
author={Ling, Wang and Yogatama, Dani and Dyer, Chris and Blunsom, Phil},
journal={ACL},
year={2017}
}

### Contributions

Thanks to @arkhalid for adding this dataset.

None yet