@inproceedings{schaefer-stede-2021-upappliedcl,
title = "{UPA}pplied{CL} at {G}erm{E}val 2021: Identifying Fact-Claiming and Engaging {F}acebook Comments Using Transformers",
author = "Schaefer, Robin and
Stede, Manfred",
booktitle = "Proceedings of the GermEval 2021 Shared Task on the Identification of Toxic, Engaging, and Fact-Claiming Comments",
month = sep,
year = "2021",
address = "Duesseldorf, Germany",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.germeval-1.2",
pages = "13--18",
abstract = "In this paper we present UPAppliedCL{'}s contribution to the GermEval 2021 Shared Task. In particular, we participated in Subtasks 2 (Engaging Comment Classification) and 3 (Fact-Claiming Comment Classification). While acceptable results can be obtained by using unigrams or linguistic features in combination with traditional machine learning models, we show that for both tasks transformer models trained on fine-tuned BERT embeddings yield best results.",
}
Markdown (Informal)
[UPAppliedCL at GermEval 2021: Identifying Fact-Claiming and Engaging Facebook Comments Using Transformers](https://aclanthology.org/2021.germeval-1.2) (Schaefer & Stede, GermEval 2021)
ACL