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

<p>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 &ldquo;construct a shared meaning of the past, present, and future of international politics to shape the behavior of domestic and international actors&rdquo;</p> <p>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.</p> <p>These are the few-shot training datasets for Englsih and Spanish.</p> <p>These files don't contain the narratives description. You can find them in the testing dataset:</p> <p>Pe&ntilde;as, A., Fraile-Hern&aacute;ndez, J. M., Moral, P., Rodrigo, &Aacute;., Deriu, J., Sharma, R., Centeno, R., Rodr&iacute;guez-Garc&iacute;a, R., Giedemann, P., &amp; Reyes-Montesinos, J. (2024). DIPROMATS 2024 - Shared Task 2: testing data for narrative identification (1.0.0) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.12663310" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12663310</a></p>