  Natural language understanding (NLU) and Natural language generation (NLG)
tasks hold a strong dual relationship, where NLU aims at predicting semantic
labels based on natural language utterances and NLG does the opposite. The
prior work mainly focused on exploiting the duality in model training in order
to obtain the models with better performance. However, regarding the
fast-growing scale of models in the current NLP area, sometimes we may have
difficulty retraining whole NLU and NLG models. To better address the issue,
this paper proposes to leverage the duality in the inference stage without the
need of retraining. The experiments on three benchmark datasets demonstrate the
effectiveness of the proposed method in both NLU and NLG, providing the great
potential of practical usage.
