Sequential dialogue act recognition for Arabic argumentative debates

Samira Ben Dbabis, Hatem Ghorbel, Lamia Hadrich Belguith


Dialogue act recognition remains a primordial task that helps user to automatically identify participants’ intentions. In this paper, we propose a sequential approach consisting of segmentation followed by annotation process to identify dialogue acts within Arabic politic debates.To perform DA recognition, we used the CARD corpus labeled using the SADA annotation schema. Segmentation and annotation tasks were then carried out using Conditional Random Fields probabilistic models as they prove high performance in segmenting and labeling sequential data. Learning results are notably important for the segmentation task (F-score=97.9%) and relatively reliable within the annotation process (fscore= 63.4%) given the complexity of identifying argumentative tags and the presence of disfluencies in spoken conversations.

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