Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task

Understand

The adoption of pre-trained language models in task-oriented dialogue systems has resulted in significant enhancements of their text generation abilities.

• However, these architectures are slow to use because of the large number of trainable parameters and can sometimes fail to generate diverse responses.
• To address these limitations, we propose two models with auxiliary tasks for response selection - (1) distinguishing distractors from ground truth responses and (2) distinguishing synthetic responses from ground truth labels.
• They achieve state-of-the-art results on the MultiWOZ 2.1 dataset with combined scores of 107.5 and 108.3 and outperform a baseline with three times more parameters.