Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue
This work addresses data scarcity for researchers and developers building end-to-end task-oriented dialogue systems, offering an incremental improvement over existing methods.
The authors tackled the problem of limited annotated data for training task-oriented dialogue systems by proposing four automatic data augmentation approaches at word and sentence levels, achieving state-of-the-art results with significant improvements in Success F1 scores on CamRest676 and KVRET datasets.
The training of task-oriented dialogue systems is often confronted with the lack of annotated data. In contrast to previous work which augments training data through expensive crowd-sourcing efforts, we propose four different automatic approaches to data augmentation at both the word and sentence level for end-to-end task-oriented dialogue and conduct an empirical study on their impact. Experimental results on the CamRest676 and KVRET datasets demonstrate that each of the four data augmentation approaches is able to obtain a significant improvement over a strong baseline in terms of Success F1 score and that the ensemble of the four approaches achieves the state-of-the-art results in the two datasets. In-depth analyses further confirm that our methods adequately increase the diversity of user utterances, which enables the end-to-end model to learn features robustly.