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.

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"
}

• 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

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.