Speech acts and Communicative Intentions for Urgency Detection

Enzo Laurenti (1) , Nils Bourgon (2, 3) , Farah Benamara (2, 4) , Alda Mari (1) , Véronique Moriceau (2, 4) , Camille Courgeon (1)

1 IJN - Institut Jean-Nicod
2 IRIT-MELODI - MEthodes et ingénierie des Langues, des Ontologies et du DIscours
3 CNRS - Centre National de la Recherche Scientifique
4 UT3 - Université Toulouse III - Paul Sabatier

Recognizing speech acts (SA) is crucial for capturing meaning beyond what is said, making communicative intentions particularly relevant to identify urgent messages. This paper attempts to measure for the first time the impact of SA on urgency detection during crises,006in tweets. We propose a new dataset annotated for both urgency and SA, and develop several deep learning architectures to inject SA into urgency detection while ensuring models generalisability. Our results show that taking speech acts into account in tweet analysis improves information type detection in an out-of-type configuration where models are evaluated in unseen event types during training. These results are encouraging and constitute a first step towards SA-aware disaster management in social media.

Domaines: Intelligence artificielle [cs.AI]; Apprentissage [cs.LG]; Traitement du texte et du document

Enzo Laurenti, Nils Bourgon, Farah Benamara, Alda Mari, Véronique Moriceau, et al.. Speech acts and Communicative Intentions for Urgency Detection. 11th Joint Conference on Lexical and Computational Semantics (*SEM 2022), Jul 2022, Seatle, United States. ⟨10.18653/v1/2022.starsem-1.25⟩. ⟨hal-03730461⟩