| # AQUA-RAT (Algebra Question Answering with Rationales) Dataset | |
| This dataset contains the algebraic word problems with rationales described in our paper: | |
| [Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom. (2017) Program Induction by Rationale Generation: Learning to Solve and Explain Algebraic Word Problems. In Proc. ACL.](https://arxiv.org/pdf/1705.04146.pdf) | |
| The dataset consists of about 100,000 algebraic word problems with natural language rationales. Each problem is a json object consisting of four parts: | |
| * `question` - A natural language definition of the problem to solve | |
| * `options` - 5 possible options (A, B, C, D and E), among which one is correct | |
| * `rationale` - A natural language description of the solution to the problem | |
| * `correct` - The correct option | |
| Here is an example of a problem object: | |
| { | |
| "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" | |
| } | |
| ## Files | |
| * `train.json` -> untokenized training set | |
| * `train.tok.json` -> tokenized training set | |
| * `dev.json` -> untokenized development set | |
| * `dev.tok.json` -> tokenized development set | |
| * `test.json` -> untokenized test set | |
| * `test.tok.json` -> tokenized test set | |
| ## Note | |
| This dataset has been fully crowdsourced, as described using the technique in the paper (Ling et al., 2017). The initial published results included in the paper were derived from a previous version of this dataset that cannot be released in full, and results using the published system will differ. Results using our published system will be forthcoming. | |