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In modular dialogue systems, natural language understanding (NLU) and naturallanguage generation (NLG) are two critical components, where NLU extracts thesemantics from the given texts and NLG is to construct corresponding naturallanguage sentences based on the input semantic representations. However, thedual property between understanding and generation has been rarely explored.The prior work is the first attempt that utilized the duality between NLU andNLG 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 effectivelyexploit such duality, providing flexibility of incorporating both supervisedand unsupervised learning algorithms to train language understanding andgeneration models in a joint fashion. The benchmark experiments demonstratethat the proposed approach is capable of boosting the performance of both NLUand NLG.

Towards Unsupervised Language Understanding and Generation by Joint Dual Learning