  Mathematical reasoning---a core ability within human intelligence---presents
some unique challenges as a domain: we do not come to understand and solve
mathematical problems primarily on the back of experience and evidence, but on
the basis of inferring, learning, and exploiting laws, axioms, and symbol
manipulation rules. In this paper, we present a new challenge for the
evaluation (and eventually the design) of neural architectures and similar
system, developing a task suite of mathematics problems involving sequential
questions and answers in a free-form textual input/output format. The
structured nature of the mathematics domain, covering arithmetic, algebra,
probability and calculus, enables the construction of training and test splits
designed to clearly illuminate the capabilities and failure-modes of different
architectures, as well as evaluate their ability to compose and relate
knowledge and learned processes. Having described the data generation process
and its potential future expansions, we conduct a comprehensive analysis of
models from two broad classes of the most powerful sequence-to-sequence
architectures and find notable differences in their ability to resolve
mathematical problems and generalize their knowledge.
