Fabian Billert


2023

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HHU at SemEval-2023 Task 3: An Adapter-based Approach for News Genre Classification
Fabian Billert | Stefan Conrad
Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)

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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Team HHU at the FinNLP-2023 ML-ESG Task: A Multi-Model Approach to ESG-Key-Issue Classification
Fabian Billert | Stefan Conrad
Proceedings of the Fifth Workshop on Financial Technology and Natural Language Processing and the Second Multimodal AI For Financial Forecasting

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Exploring Knowledge Composition for ESG Impact Type Determination
Fabian Billert | Stefan Conrad
Proceedings of the Sixth Workshop on Financial Technology and Natural Language Processing

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.