  We proposed Neural Enquirer as a neural network architecture to execute a
natural language (NL) query on a knowledge-base (KB) for answers. Basically,
Neural Enquirer finds the distributed representation of a query and then
executes it on knowledge-base tables to obtain the answer as one of the values
in the tables. Unlike similar efforts in end-to-end training of semantic
parsers, Neural Enquirer is fully "neuralized": it not only gives
distributional representation of the query and the knowledge-base, but also
realizes the execution of compositional queries as a series of differentiable
operations, with intermediate results (consisting of annotations of the tables
at different levels) saved on multiple layers of memory. Neural Enquirer can be
trained with gradient descent, with which not only the parameters of the
controlling components and semantic parsing component, but also the embeddings
of the tables and query words can be learned from scratch. The training can be
done in an end-to-end fashion, but it can take stronger guidance, e.g., the
step-by-step supervision for complicated queries, and benefit from it. Neural
Enquirer is one step towards building neural network systems which seek to
understand language by executing it on real-world. Our experiments show that
Neural Enquirer can learn to execute fairly complicated NL queries on tables
with rich structures.
