Abstract
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.
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CITATION STYLE
Billert, F., & Conrad, S. (2023). HHU at SemEval-2023 Task 3: An Adapter-based Approach for News Genre Classification. In 17th International Workshop on Semantic Evaluation, SemEval 2023 - Proceedings of the Workshop (pp. 1166–1171). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.semeval-1.162
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