  Disfluency detection is a critical task in real-time dialogue systems.
However, despite its importance, it remains a relatively unexplored field,
mainly due to the lack of appropriate datasets. At the same time, existing
datasets suffer from various issues, including class imbalance issues, which
can significantly affect the performance of the model on rare classes, as it is
demonstrated in this paper. To this end, we propose LARD, a method for
generating complex and realistic artificial disfluencies with little effort.
The proposed method can handle three of the most common types of disfluencies:
repetitions, replacements and restarts. In addition, we release a new
large-scale dataset with disfluencies that can be used on four different tasks:
disfluency detection, classification, extraction and correction. Experimental
results on the LARD dataset demonstrate that the data produced by the proposed
method can be effectively used for detecting and removing disfluencies, while
also addressing limitations of existing datasets.
