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.

该研究通过四种自动方法在单词和句子级别上增强数据，提高目标导向对话模型的数据效果，并在两个数据集上进行实验，结果表明四种数据扩充方法在 Success F1 score 方面均能显著提高。进一步分析确认，增加用户话语的多样性可以使端到端模型学习特征更为稳健。