This paper presents a novel approach to learning to solve simple arithmetic word problems. Our system, ARIS, analyzes each of the sentences in the problem statement to identify the relevant variables and their values. ARIS then maps this information into an equation that represents the problem, and enables its (trivial) solution as shown in this figure. The paper analyzes the arithmetic-word problems genre, identifying seven categories of verbs used in such problems.

ARIS learns to categorize verbs with 81.2% accuracy, and is able to solve 77.7 of the problems in a corpus of standard primary school test questions. We report the first learning results on this task without reliance on predefined templates and make our data publicly available.



    The research was supported by the Allen Institute
    for AI, and grants from the NSF (IIS-1352249)
    and UW-RRF (65-2775).


    You can find our dataset here and our code here!