Creating effective and reliable task-oriented dialog systems (ToDSs) is
challenging, not only because of the complex structure of these systems, but
also due to the scarcity of training data, especially when several modules need
to be trained separately, each one with its own input/output training examples.
Data augmentation (DA), whereby synthetic training examples are added to the
training data, has been successful in other NLP systems, but has not been
explored as extensively in ToDSs. We empirically evaluate the effectiveness of
DA methods in an end-to-end ToDS setting, where a single system is trained to
handle all processing stages, from user inputs to system outputs. We experiment
with two ToDSs (UBAR, GALAXY) on two datasets (MultiWOZ, KVRET). We consider
three types of DA methods (word-level, sentence-level, dialog-level), comparing
eight DA methods that have shown promising results in ToDSs and other NLP
systems. We show that all DA methods considered are beneficial, and we
highlight the best ones, also providing advice to practitioners. We also
introduce a more challenging few-shot cross-domain ToDS setting, reaching
similar conclusions.

在任务导向型对话系统中，通过数据增强方法在端到端设置下进行实证评估，证明了所有考虑的数据增强方法都是有益的，并提供了最佳方法和实践建议，同时引入了一个更具挑战性的少样本跨领域任务导向型对话系统的设置。