Multijugate Dual Learning for Low-Resource Task-Oriented Dialogue System (2023)

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.

## introduction with the emergence of dialogue data @xcite , and the evolution of pre-trained language models @xcite , end-to-end task-oriented dialogue (tod) systems @xcite @xcite gradually replaced the previous modular cascading dialogue systems @xcite . the end-to-end tod system adopts a uniform training objective, preventing the error propagation problem in pipelined dialogue systems @xcite . nonetheless, the end-to-end paradigm requires more training data to perform better @xcite . meanwhile, tod data is enormously expensive to annotate @xcite as it simultaneously contains dialogue state tracking, dialogue action prediction, and response generation. it is also expensive to annotate large amounts of complicated dialogue data for ## conclusion we propose a novel multijugate dual learning for task-oriented dialogues in low-resource scenarios. exploiting the duality between deterministic dialogue states and uncertain utterances enables the entity alignment information in heterogeneous data to be fully exploited. meanwhile, paraphraseenhanced multijugate dual learning alleviates the spurious correlation of shallow pattern statistics. experiments on several tod datasets show that the proposed method achieves state-of-the-art results in both end-to-end response generation and dialogue state tracking in low-resource scenarios.