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The adoption of pre-trained language models in task-oriented dialogue systemshas resulted in significant enhancements of their text generation abilities.However, these architectures are slow to use because of the large number oftrainable parameters and can sometimes fail to generate diverse responses. Toaddress these limitations, we propose two models with auxiliary tasks forresponse selection - (1) distinguishing distractors from ground truth responsesand (2) distinguishing synthetic responses from ground truth labels. Theyachieve state-of-the-art results on the MultiWOZ 2.1 dataset with combinedscores of 107.5 and 108.3 and outperform a baseline with three times moreparameters. We publish reproducible code and checkpoints and discuss theeffects of applying auxiliary tasks to T5-based architectures.

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