  In modular dialogue systems, natural language understanding (NLU) and natural
language generation (NLG) are two critical components, where NLU extracts the
semantics from the given texts and NLG is to construct corresponding natural
language sentences based on the input semantic representations. However, the
dual property between understanding and generation has been rarely explored.
The prior work is the first attempt that utilized the duality between NLU and
NLG to improve the performance via a dual supervised learning framework.
However, the prior work still learned both components in a supervised manner,
instead, this paper introduces a general learning framework to effectively
exploit such duality, providing flexibility of incorporating both supervised
and unsupervised learning algorithms to train language understanding and
generation models in a joint fashion. The benchmark experiments demonstrate
that the proposed approach is capable of boosting the performance of both NLU
and NLG.
