@inproceedings{grotzinger-etal-2023-cicl,
title = "{CICL}{\_}{DMS} at {S}em{E}val-2023 Task 11: Learning With Disagreements (Le-Wi-Di)",
author = {Gr{\"o}tzinger, Dennis and
Heuschkel, Simon and
Drews, Matthias},
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-1.141",
doi = "10.18653/v1/2023.semeval-1.141",
pages = "1030--1036",
abstract = "In this system paper, we describe our submission for the 11th task of SemEval2023: Learning with Disagreements, or Le-Wi-Di for short. In the task, the assumption that there is a single gold label in NLP tasks such as hate speech or misogyny detection is challenged, and instead the opinions of multiple annotators are considered. The goal is instead to capture the agreements/disagreements of the annotators. For our system, we utilize the capabilities of modern large-language models as our backbone and investigate various techniques built on top, such as ensemble learning, multi-task learning, or Gaussian processes. Our final submission shows promising results and we achieve an upper-half finish.",
}
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<abstract>In this system paper, we describe our submission for the 11th task of SemEval2023: Learning with Disagreements, or Le-Wi-Di for short. In the task, the assumption that there is a single gold label in NLP tasks such as hate speech or misogyny detection is challenged, and instead the opinions of multiple annotators are considered. The goal is instead to capture the agreements/disagreements of the annotators. For our system, we utilize the capabilities of modern large-language models as our backbone and investigate various techniques built on top, such as ensemble learning, multi-task learning, or Gaussian processes. Our final submission shows promising results and we achieve an upper-half finish.</abstract>
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%0 Conference Proceedings
%T CICL_DMS at SemEval-2023 Task 11: Learning With Disagreements (Le-Wi-Di)
%A Grötzinger, Dennis
%A Heuschkel, Simon
%A Drews, Matthias
%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 grotzinger-etal-2023-cicl
%X In this system paper, we describe our submission for the 11th task of SemEval2023: Learning with Disagreements, or Le-Wi-Di for short. In the task, the assumption that there is a single gold label in NLP tasks such as hate speech or misogyny detection is challenged, and instead the opinions of multiple annotators are considered. The goal is instead to capture the agreements/disagreements of the annotators. For our system, we utilize the capabilities of modern large-language models as our backbone and investigate various techniques built on top, such as ensemble learning, multi-task learning, or Gaussian processes. Our final submission shows promising results and we achieve an upper-half finish.
%R 10.18653/v1/2023.semeval-1.141
%U https://aclanthology.org/2023.semeval-1.141
%U https://doi.org/10.18653/v1/2023.semeval-1.141
%P 1030-1036
Markdown (Informal)
[CICL_DMS at SemEval-2023 Task 11: Learning With Disagreements (Le-Wi-Di)](https://aclanthology.org/2023.semeval-1.141) (Grötzinger et al., SemEval 2023)
ACL