LARD: Large-scale Artificial Disfluency Generation

Tatiana Passali, Thanassis Mavropoulos, Grigorios Tsoumakas, Georgios Meditskos, Stefanos Vrochidis

Abstract
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.
Anthology ID:
2022.lrec-1.249
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
2327–2336
Language:
URL:
https://aclanthology.org/2022.lrec-1.249
DOI:
Bibkey:
Cite (ACL):
Tatiana Passali, Thanassis Mavropoulos, Grigorios Tsoumakas, Georgios Meditskos, and Stefanos Vrochidis. 2022. LARD: Large-scale Artificial Disfluency Generation. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2327–2336, Marseille, France. European Language Resources Association.
Cite (Informal):
LARD: Large-scale Artificial Disfluency Generation (Passali et al., LREC 2022)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.249.pdf
Code
 tatianapassali/artificial-disfluency-generation
Data
SGD
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