@inproceedings{hromadka-etal-2023-kinitveraai,
title = "{KI}n{ITV}era{AI} at {S}em{E}val-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection",
author = "Hromadka, Timo and
Smolen, Timotej and
Remis, Tomas and
Pecher, Branislav and
Srba, Ivan",
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-1.86",
doi = "10.18653/v1/2023.semeval-1.86",
pages = "629--637",
abstract = "This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of the input data and a large number of 23 predicted labels (causing a lack of labelled data for some language-label combinations), we opted for fine-tuning pre-trained transformer-based language models. Conducting multiple experiments, we find the best configuration, which consists of large multilingual model (XLM-RoBERTa large) trained jointly on all input data, with carefully calibrated confidence thresholds for seen and surprise languages separately. Our final system performed the best on 6 out of 9 languages (including two surprise languages) and achieved highly competitive results on the remaining three languages.",
}
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%0 Conference Proceedings
%T KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection
%A Hromadka, Timo
%A Smolen, Timotej
%A Remis, Tomas
%A Pecher, Branislav
%A Srba, Ivan
%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 hromadka-etal-2023-kinitveraai
%X This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of the input data and a large number of 23 predicted labels (causing a lack of labelled data for some language-label combinations), we opted for fine-tuning pre-trained transformer-based language models. Conducting multiple experiments, we find the best configuration, which consists of large multilingual model (XLM-RoBERTa large) trained jointly on all input data, with carefully calibrated confidence thresholds for seen and surprise languages separately. Our final system performed the best on 6 out of 9 languages (including two surprise languages) and achieved highly competitive results on the remaining three languages.
%R 10.18653/v1/2023.semeval-1.86
%U https://aclanthology.org/2023.semeval-1.86
%U https://doi.org/10.18653/v1/2023.semeval-1.86
%P 629-637
Markdown (Informal)
[KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection](https://aclanthology.org/2023.semeval-1.86) (Hromadka et al., SemEval 2023)
ACL