Narratives include a rich source of events unfolding over time and context. Automatic understanding of these events provides a summarised comprehension of the narrative for further computation (such as reasoning). In this paper, we study the Information Status (IS) of the events and propose a novel challenging task: the automatic identification of new events in a narrative. We define an event as a triplet of subject, predicate, and object. The event is categorized as new with respect to the discourse context and whether it can be inferred through commonsense reasoning. We annotated a publicly available corpus of narratives with the new events at sentence level using human annotators. We present the annotation protocol and study the quality of the annotation and the difficulty of the task. We publish the annotated dataset, annotation materials, and machine learning baseline models for the task of new event extraction for narrative understanding.
What{'}s New? Identifying the Unfolding of New Events in a Narrative / Mousavi, Seyed Mahed; Tanaka, Shohei; Roccabruna, Gabriel; Yoshino, Koichiro; Nakamura, Satoshi; Riccardi, Giuseppe. - (2023), pp. 1-10. (Intervento presentato al convegno 5th Workshop on Narrative Understanding tenutosi a toronto canada nel July 14, 2023) [10.18653/v1/2023.wnu-1.1].
What{'}s New? Identifying the Unfolding of New Events in a Narrative
Seyed Mahed Mousavi
Primo
;Gabriel Roccabruna;Giuseppe Riccardi
2023-01-01
Abstract
Narratives include a rich source of events unfolding over time and context. Automatic understanding of these events provides a summarised comprehension of the narrative for further computation (such as reasoning). In this paper, we study the Information Status (IS) of the events and propose a novel challenging task: the automatic identification of new events in a narrative. We define an event as a triplet of subject, predicate, and object. The event is categorized as new with respect to the discourse context and whether it can be inferred through commonsense reasoning. We annotated a publicly available corpus of narratives with the new events at sentence level using human annotators. We present the annotation protocol and study the quality of the annotation and the difficulty of the task. We publish the annotated dataset, annotation materials, and machine learning baseline models for the task of new event extraction for narrative understanding.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione