  Natural language understanding (NLU) and natural language generation (NLG)
are both critical research topics in the NLP field. Natural language
understanding is to extract the core semantic meaning from the given
utterances, while natural language generation is opposite, of which the goal is
to construct corresponding sentences based on the given semantics. However,
such dual relationship has not been investigated in the literature. This paper
proposes a new learning framework for language understanding and generation on
top of dual supervised learning, providing a way to exploit the duality. The
preliminary experiments show that the proposed approach boosts the performance
for both tasks.
