  Dialogue data in real scenarios tend to be sparsely available, rendering
data-starved end-to-end dialogue systems trained inadequately. We discover that
data utilization efficiency in low-resource scenarios can be enhanced by mining
alignment information uncertain utterance and deterministic dialogue state.
Therefore, we innovatively implement dual learning in task-oriented dialogues
to exploit the correlation of heterogeneous data. In addition, the one-to-one
duality is converted into a multijugate duality to reduce the influence of
spurious correlations in dual training for generalization. Without introducing
additional parameters, our method could be implemented in arbitrary networks.
Extensive empirical analyses demonstrate that our proposed method improves the
effectiveness of end-to-end task-oriented dialogue systems under multiple
benchmarks and obtains state-of-the-art results in low-resource scenarios.
