Multimodality, or how human communication and interaction combine multiple forms of expression, is studied across diverse fields of research. Many of these fields have underlined the need for large, richly annotated multimodal corpora to support empirical research. While language resources are increasingly annotated using microtask crowdsourcing, multimodal corpora remain largely reliant on expert annotators, which creates a bottleneck for scalability and broad applicability. This paper presents a novel hybrid approach to multimodal corpus annotation, leveraging the efficiency of microtask pipelines while preserving theoretical rigour. Our approach decomposes the annotation process into sequences of simple, well-instructed tasks, which are then performed by locally recruited non-expert annotators. We demonstrate the feasibility of this approach by presenting a pipeline for annotating the multimodal structure of school textbooks.

This paper presents an approach to computationally detecting face-threatening and paired actions in asynchronous online conversations. Action detection has been widely studied for synchronous chats. However, there are fewer models or datasets for asynchronous conversations, and they have not included some of the face-threatening actions central to online conversations involving misbehavior like trolling. We examine asynchronous crisis news related online conversations in Finnish, providing an annotation scheme for identifying central actions used in this conversational context. An important contribution is to include face-threatening actions in the scheme, and training computational classifiers for their detection with improved performance compared to prior work. We illustrate that face-threatening actions are important for analyzing conversations related to crisis news. We show that for computational action detection, it is essential to be able to represent how multiple actions may be performed within one comment, and how ambiguity in the expression of actions often leads to multiple possible label interpretations. Annotating actions using scores helps to reflect these characteristics. We also find that an ensemble of models trained on individual annotators’ annotations can best represent multiple potential interpretations of action labels. These are especially relevant for face-threatening actions.

This paper describes the system submitted by our team to the Multilingual Euphemism Detection Shared Task for the Fourth Workshop on Figurative Language Processing (FigLang 2024). We propose a novel model for multilingual euphemism detection, combining contextual and behavior-related features. The system classifies texts that potentially contain euphemistic terms with an ensemble classifier based on outputs from behavior-related fine-tuned models. Our results show that, for this kind of task, our model outperforms baselines and state-of-the-art euphemism detection methods. As for the leader-board, our classification model achieved a macro averaged F1 score of [anonymized], reaching the [anonymized] place.