Abstract
Natural language understanding (NLU) and
natural language generation (NLG) are both
critical research topics in the NLP and dialogue fields. 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 literature. This paper proposes a novel learning
framework for natural 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, demonstrating the effectiveness of
the dual relationship.