Controllable User Dialogue Act Augmentation for Dialogue State Tracking
This work addresses generalization issues in dialogue state tracking for conversational AI systems, but it is incremental as it builds on prior data augmentation methods.
The paper tackled the problem of poor generalization in dialogue state tracking due to limited user utterance types in data augmentation by proposing controllable user dialogue act augmentation (CUDA-DST) to generate diverse user behaviors, resulting in improved performance and state-of-the-art results on MultiWOZ 2.1.
Prior work has demonstrated that data augmentation is useful for improving dialogue state tracking. However, there are many types of user utterances, while the prior method only considered the simplest one for augmentation, raising the concern about poor generalization capability. In order to better cover diverse dialogue acts and control the generation quality, this paper proposes controllable user dialogue act augmentation (CUDA-DST) to augment user utterances with diverse behaviors. With the augmented data, different state trackers gain improvement and show better robustness, achieving the state-of-the-art performance on MultiWOZ 2.1