  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.
