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.

本文介绍了一种联合学习框架，旨在通过一种新颖的双重信息最大化方法对两个任务 (自然语言生成与语义分析) 进行正则化学习，进一步将其扩展到半监督学习，并在对话管理和代码生成 (以及总结) 三个数据集上进行了实验，结果表明 DIM 可以提高两个任务的学习性能。