Existing approaches for disfluency detection typically require the existence
of large annotated datasets. However, current datasets for this task are
limited, suffer from class imbalance, and lack some types of disfluencies that
can be encountered in real-world scenarios. This work proposes LARD, a method
for automatically generating artificial disfluencies from fluent text. LARD can
simulate all the different types of disfluencies (repetitions, replacements and
restarts) based on the reparandum/interregnum annotation scheme. In addition,
it incorporates contextual embeddings into the disfluency generation to produce
realistic context-aware artificial disfluencies. Since the proposed method
requires only fluent text, it can be used directly for training, bypassing the
requirement of annotated disfluent data. Our empirical evaluation demonstrates
that LARD can indeed be effectively used when no or only a few data are
available. Furthermore, our detailed analysis suggests that the proposed method
generates realistic disfluencies and increases the accuracy of existing
disfluency detectors.

本文提出了一种名为 LARD 的方法，该方法可以从流畅的文本中自动生成人工语言障碍，并将上下文嵌入到混淆生成中以产生逼真的上下文感知人工语言障碍，从而绕过标注数据的要求。我们的实验证明 LARD 可以在没有或只有少量数据时有效地使用，并增加了现有混淆检测器的准确性。