  Semantic parsing aims to transform natural language (NL) utterances into
formal meaning representations (MRs), whereas an NL generator achieves the
reverse: producing a NL description for some given MRs. Despite this intrinsic
connection, the two tasks are often studied separately in prior work. In this
paper, we model the duality of these two tasks via a joint learning framework,
and demonstrate its effectiveness of boosting the performance on both tasks.
Concretely, we propose a novel method of dual information maximization (DIM) to
regularize the learning process, where DIM empirically maximizes the
variational lower bounds of expected joint distributions of NL and MRs. We
further extend DIM to a semi-supervision setup (SemiDIM), which leverages
unlabeled data of both tasks. Experiments on three datasets of dialogue
management and code generation (and summarization) show that performance on
both semantic parsing and NL generation can be consistently improved by DIM, in
both supervised and semi-supervised setups.
