This paper describes our approach for Subtask 1 of Task 3 at SemEval-2023. In this subtask, task participants were asked to classify multilingual news articles for one of three classes: Reporting, Opinion Piece or Satire. By training an AdapterFusion layer composing the task-adapters from different languages, we successfully combine the language-exclusive knowledge and show that this improves the results in nearly all cases, including in zero-shot scenarios.

In this paper, we discuss our (Team HHU’s) submission to the Multi-Lingual ESG Impact Type Identification task (ML-ESG-2). The goal of this task is to determine if an ESG-related news article represents an opportunity or a risk. We use an adapter-based framework in order to train multiple adapter modules which capture different parts of the knowledge present in the training data. Experimenting with various Adapter Fusion setups, we focus both on combining the ESG-aspect-specific knowledge, and on combining the language-specific-knowledge. Our results show that in both cases, it is possible to effectively compose the knowledge in order to improve the impact type determination.