DIPROMATS 2024 - Shared Task 2: few-shot training data for narrative identification

Abstract

Narratives are causally connected sequences of events that are selected and evaluated as meaningful for a particular audience. They make sense of the world by identifying the significance of people, places, objects, and events in time. In international relations, international actors create strategic narratives to “construct a shared meaning of the past, present, and future of international politics to shape the behavior of domestic and international actors” DIPROMATS 2024 Task 2 is a multiclass multilabel classification problem. Given a series of predefined narratives of each international actor, systems must determine which narrative the tweets belong to. Systems will receive the description of each narrative and a few examples of tweets in both languages (English and Spanish) that belong to each of them (few-shot learning). A tweet may be associated with one, several or none of the narratives. These are the few-shot training datasets for Englsih and Spanish. These files don't contain the narratives description. You can find them in the testing dataset: Peñas, A., Fraile-Hernández, J. M., Moral, P., Rodrigo, Á., Deriu, J., Sharma, R., Centeno, R., Rodríguez-García, R., Giedemann, P., & Reyes-Montesinos, J. (2024). DIPROMATS 2024 - Shared Task 2: testing data for narrative identification (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.12663310