Pielka, MarenMarenPielkaLadi, AnnaAnnaLadiChapman, Clayton LeroyClayton LeroyChapmanBrito, EduardoEduardoBritoRamamurthy, RajkumarRajkumarRamamurthyMayer, PaulPaulMayerWahab, AbdulAbdulWahabSifa, RafetRafetSifaBauckhage, ChristianChristianBauckhage2025-12-192025-12-1920209781952148408https://publica.fraunhofer.de/handle/publica/5024322-s2.0-85113673372The FinCausal 2020 shared task aims to detect causality on financial news and identify those parts of the causal sentences related to the underlying cause and effect. We apply ensemble-based and sequence tagging methods for identifying causality, and extracting causal subsequences. Our models yield promising results on both sub-tasks, with the prospect of further improvement given more time and computing resources. With respect to task 1, we achieved an F1 score of 0.9429 on the evaluation data, and a corresponding ranking of 12/14. For task 2, we were ranked 6/10, with an F1 score of 0.76 and an ExactMatch score of 0.1912.enfalseconference paper