@inproceedings{konca-etal-2024-german-srl,
title = "{G}erman {SRL}: Corpus Construction and Model Training",
author = "Konca, Maxim and
Luecking, Andy and
Mehler, Alexander",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.682",
pages = "7717--7727",
abstract = "A useful semantic role-annotated resource for training semantic role models for the German language is missing. We point out some problems of previous resources and provide a new one due to a combined translation and alignment process: The gold standard CoNLL-2012 semantic role annotations are translated into German. Semantic role labels are transferred due to alignment models. The resulting dataset is used to train a German semantic role model. With F1-scores around 0.7, the major roles achieve competitive evaluation scores, but avoid limitations of previous approaches. The described procedure can be applied to other languages as well.",
}
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<abstract>A useful semantic role-annotated resource for training semantic role models for the German language is missing. We point out some problems of previous resources and provide a new one due to a combined translation and alignment process: The gold standard CoNLL-2012 semantic role annotations are translated into German. Semantic role labels are transferred due to alignment models. The resulting dataset is used to train a German semantic role model. With F1-scores around 0.7, the major roles achieve competitive evaluation scores, but avoid limitations of previous approaches. The described procedure can be applied to other languages as well.</abstract>
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%0 Conference Proceedings
%T German SRL: Corpus Construction and Model Training
%A Konca, Maxim
%A Luecking, Andy
%A Mehler, Alexander
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F konca-etal-2024-german-srl
%X A useful semantic role-annotated resource for training semantic role models for the German language is missing. We point out some problems of previous resources and provide a new one due to a combined translation and alignment process: The gold standard CoNLL-2012 semantic role annotations are translated into German. Semantic role labels are transferred due to alignment models. The resulting dataset is used to train a German semantic role model. With F1-scores around 0.7, the major roles achieve competitive evaluation scores, but avoid limitations of previous approaches. The described procedure can be applied to other languages as well.
%U https://aclanthology.org/2024.lrec-main.682
%P 7717-7727
Markdown (Informal)
[German SRL: Corpus Construction and Model Training](https://aclanthology.org/2024.lrec-main.682) (Konca et al., LREC-COLING 2024)
ACL
- Maxim Konca, Andy Luecking, and Alexander Mehler. 2024. German SRL: Corpus Construction and Model Training. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7717–7727, Torino, Italia. ELRA and ICCL.