Abstract
We present NarratEX, a dataset designed for the task of explaining the choice of the Dominant Narrative in a news article, and intended to support the research community in addressing challenges such as discourse polarization and propaganda detection. Our dataset comprises 1,056 news articles in four languages, Bulgarian, English, Portuguese, and Russian, covering two globally significant topics: the Ukraine-Russia War (URW) and Climate Change (CC). Each article is manually annotated with a dominant narrative and sub-narrative labels, and an explanation justifying the chosen labels. We describe the dataset, the process of its creation, and its characteristics. We present experiments with two new proposed tasks: Explaining Dominant Narrative based on Text, which involves writing a concise paragraph to justify the choice of the dominant narrative and sub-narrative of a given text, and Inferring Dominant Narrative from Explanation, which involves predicting the appropriate dominant narrative category based on an explanatory text. The proposed dataset is a valuable resource for advancing research on detecting and mitigating manipulative content, while promoting a deeper understanding of how narratives influence public discourse.

Abstract
The development of a robust annotation scheme and corresponding guidelines is crucial for pro- ducing annotated datasets that advance both lin- guistic and computational research. This paper presents a case study that outlines a method- ology for designing an annotation scheme and its guidelines, specifically aimed at represent- ing morphosyntactic and semantic information regarding temporal features, as well as medi- cal information in medical reports written in Portuguese. We detail a multi-step process that includes reviewing existing frameworks, con- ducting an annotation experiment to determine the optimal approach, and designing a model based on these findings. We validated the ap- proach through a pilot experiment where we assessed the reliability and applicability of the annotation scheme and guidelines. In this ex- periment, two annotators independently anno- tated a patient's medical report consisting of six documents using the proposed model, while a curator established the ground truth. The analy- sis of inter-annotator agreement and the annota- tion results enabled the identification of sources of human variation and provided insights for further refinement of the annotation scheme and guidelines.

Abstract
We introduce a novel multilingual hierarchical corpus annotated for entity framing and role portrayal in news articles. The dataset uses a unique taxonomy inspired by storytelling elements, comprising 22 fine-grained roles, or archetypes, nested within three main categories: protagonist, antagonist, and innocent. Each archetype is carefully defined, capturing nuanced portrayals of entities such as guardian, martyr, and underdog for protagonists; tyrant, deceiver, and bigot for antagonists; and victim, scapegoat, and exploited for innocents. The dataset includes 1,378 recent news articles in five languages (Bulgarian, English, Hindi, European Portuguese, and Russian) focusing on two critical domains of global significance: the Ukraine-Russia War and Climate Change. Over 5,800 entity mentions have been annotated with role labels. This dataset serves as a valuable resource for research into role portrayal and has broader implications for news analysis. We describe the characteristics of the dataset and the annotation process, and we report evaluation results on fine-tuned state-of-the-art multilingual transformers and hierarchical zero-shot learning using LLMs at the level of a document, a paragraph, and a sentence.

Abstract
The main objective of this paper is to introduce a new language resource for some varieties of Portuguese - European, Brazilian, Mozambican, and Angolan - and for British English, called DRIPPS (Discourse Relations In Perfect Participial Sentences). The corpus DRIPPS comprises, at the moment, 993 adverbial perfect participial sentences annotated with Discourse Relations and with the following Discourse Relational Devices: connectors, ordering of the clauses, temporal relations, tenses, and aspectual types. Additionally, an application with a Graphical User Interface (GUI) has been developed not only to browse and manipulate the corpus but also to allow the activation of specific Discourse Relation constraints, thereby selecting specific cases from the data set that can be analyzed separately. Besides calculating simple counts and percentages, insightful statistical graphs can be generated and visualized on the fly from the combination of the user-selected constraints and the loaded corpora. The application is pre-loaded with Portuguese and English cases and allows to import/load further cases from different languages/ varieties.

Abstract
Discourse markers carry information about the discourse structure and organization, and also signal local dependencies or epistemological stance of speaker. They provide instructions on how to interpret the discourse, and their study is paramount to understand the mechanism underlying discourse organization. This paper presents a new language resource, an ISO-based annotated multilingual parallel corpus for discourse markers. The corpus comprises nine languages, Bulgarian, Lithuanian, German, European Portuguese, Hebrew, Romanian, Polish, and Macedonian, with English as a pivot language. In order to represent the meaning of the discourse markers, we propose an annotation scheme of discourse relations from ISO 24617-8 with a plug-in to ISO 24617-2 for communicative functions. We describe an experiment in which we applied the annotation scheme to assess its validity. The results reveal that, although some extensions are required to cover all the multilingual data, it provides a proper representation of discourse markers value. Additionally, we report some relevant contrastive phenomena concerning discourse markers interpretation and role in discourse. This first step will allow us to develop deep learning methods to identify and extract discourse relations and communicative functions, and to represent that information as Linguistic Linked Open Data (LLOD).

Abstract