DIPROMATS 2024 - Shared Task 2: testing data for narrative identification
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Peñas, Anselmo
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Fraile-Hernández, Jesús M.
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Moral, Pablo
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Rodrigo, Álvaro
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Deriu, Jan
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Sharma, Rajesh
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Centeno, Roberto
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Rodríguez-García, Raquel
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Giedemann, Patrick
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Reyes-Montesinos, Julio
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Universidad Nacional de Educación a Distancia
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- 2 ZHAW Zurich University of Applied Sciences
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University of Tartu
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Éditeur: Zenodo
Année de publication: 2024
Type: Dataset
Résumé
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. The few-shot training data can be found here: https://doi.org/10.5281/zenodo.10820961 These are the testing datasets for Englsih and Spanish. They are provided without the keys so the large language models can't be contaminated. If you are interested on testing your system, write anselmo@lsi.uned.es for details on submission and leaderboards.