Abstract
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.
Abstract (translated)
实际场景中的对话数据往往很少可用，导致数据匮乏的端到端对话系统训练不足。我们发现，在资源匮乏的情况下，可以通过挖掘不确定的言词和确定性对话状态的信息，提高数据利用效率。因此，我们创新性地在任务导向的对话中实施双重学习，利用不同数据之间的相关性。此外，将一对一的二元关系转换为多视角的二元关系，以减少在双重训练中伪相关性的影响。在没有引入额外的参数的情况下，我们的方法可以应用于任意网络。广泛的实证分析表明，我们提出的这种方法在多个基准条件下改进了端到端任务导向对话系统的效力，并在资源匮乏的情况下取得了最先进的结果。
URL
https://arxiv.org/abs/2305.16106
