@inproceedings{paulissen-wendt-2023-lauri,
title = "Lauri Ingman at {S}em{E}val-2023 Task 4: A Chain Classifier for Identifying Human Values behind Arguments",
author = "Paulissen, Spencer and
Wendt, Caroline",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.27",
doi = "10.18653/v1/2023.semeval-1.27",
pages = "193--198",
abstract = "Identifying expressions of human values in textual data is a crucial albeit complicated challenge, not least because ethics are highly variable, often implicit, and transcend circumstance. Opinions, arguments, and the like are generally founded upon more than one guiding principle, which are not necessarily independent. As such, little is known about how to classify and predict moral undertones in natural language sequences. Here, we describe and present a solution to ValueEval, our shared contribution to SemEval 2023 Task 4. Our research design focuses on investigating chain classifier architectures with pretrained contextualized embeddings to detect 20 different human values in written arguments. We show that our best model substantially surpasses the classification performance of the baseline method established in prior work. We discuss limitations to our approach and outline promising directions for future work.",
}
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%0 Conference Proceedings
%T Lauri Ingman at SemEval-2023 Task 4: A Chain Classifier for Identifying Human Values behind Arguments
%A Paulissen, Spencer
%A Wendt, Caroline
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F paulissen-wendt-2023-lauri
%X Identifying expressions of human values in textual data is a crucial albeit complicated challenge, not least because ethics are highly variable, often implicit, and transcend circumstance. Opinions, arguments, and the like are generally founded upon more than one guiding principle, which are not necessarily independent. As such, little is known about how to classify and predict moral undertones in natural language sequences. Here, we describe and present a solution to ValueEval, our shared contribution to SemEval 2023 Task 4. Our research design focuses on investigating chain classifier architectures with pretrained contextualized embeddings to detect 20 different human values in written arguments. We show that our best model substantially surpasses the classification performance of the baseline method established in prior work. We discuss limitations to our approach and outline promising directions for future work.
%R 10.18653/v1/2023.semeval-1.27
%U https://aclanthology.org/2023.semeval-1.27
%U https://doi.org/10.18653/v1/2023.semeval-1.27
%P 193-198
Markdown (Informal)
[Lauri Ingman at SemEval-2023 Task 4: A Chain Classifier for Identifying Human Values behind Arguments](https://aclanthology.org/2023.semeval-1.27) (Paulissen & Wendt, SemEval 2023)
ACL