数据集:
aqua_rat
任务:
子任务:
multiple-choice-qa语言:
计算机处理:
monolingual大小:
10K<n<100K批注创建人:
crowdsourced源数据集:
original预印本库:
arxiv:1705.04146许可:
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
{ "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" }
Train | Valid | Test | |
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Examples | 97467 | 254 | 254 |
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Copyright 2017 Google Inc.
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.
@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} }
Thanks to @arkhalid for adding this dataset.