LARD: Large-scale Artificial Disfluency Generation
This addresses the lack of appropriate datasets for disfluency detection in real-time dialogue systems, though it is incremental as it builds on existing methods for data generation.
The paper tackles the problem of disfluency detection in dialogue systems by proposing LARD, a method for generating realistic artificial disfluencies, and releases a large-scale dataset that addresses class imbalance issues, showing it can effectively detect and remove disfluencies.
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.