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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, existingdatasets suffer from various issues, including class imbalance issues, whichcan significantly affect the performance of the model on rare classes, as it isdemonstrated in this paper. To this end, we propose LARD, a method forgenerating 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 newlarge-scale dataset with disfluencies that can be used on four different tasks:disfluency detection, classification, extraction and correction. Experimentalresults on the LARD dataset demonstrate that the data produced by the proposedmethod can be effectively used for detecting and removing disfluencies, whilealso addressing limitations of existing datasets.

LARD: Large-scale Artificial Disfluency Generation