@inproceedings{kuhn-etal-2024-using-pre,
title = "Using Pre-Trained Language Models in an End-to-End Pipeline for Antithesis Detection",
author = {K{\"u}hn, Ramona and
Saadi, Khouloud and
Mitrovi{\'c}, Jelena and
Granitzer, Michael},
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.1502",
pages = "17310--17320",
abstract = "Rhetorical figures play an important role in influencing readers and listeners. Some of these word constructs that deviate from the usual language structure are known to be persuasive {--} antithesis is one of them. This figure combines parallel phrases with opposite ideas or words to highlight a contradiction. By identifying this figure, persuasive actors can be better identified. For this task, we create an annotated German dataset for antithesis detection. The dataset consists of posts from a Telegram channel criticizing the COVID-19 politics in Germany. Furthermore, we propose a three-block pipeline approach to detect the figure antithesis using large language models. Our pipeline splits the text into phrases, identifies phrases with a syntactically parallel structure, and detects if these parallel phrase pairs present opposing ideas by fine-tuning the German ELECTRA model, a state-of-the-art deep learning model for the German language. Furthermore, we compare the results with multilingual BERT and German BERT. Our novel approach outperforms the state-of-the-art methods (F1-score of 50.43 {\%}) for antithesis detection by achieving an F1-score of 65.11 {\%}.",
}
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<abstract>Rhetorical figures play an important role in influencing readers and listeners. Some of these word constructs that deviate from the usual language structure are known to be persuasive – antithesis is one of them. This figure combines parallel phrases with opposite ideas or words to highlight a contradiction. By identifying this figure, persuasive actors can be better identified. For this task, we create an annotated German dataset for antithesis detection. The dataset consists of posts from a Telegram channel criticizing the COVID-19 politics in Germany. Furthermore, we propose a three-block pipeline approach to detect the figure antithesis using large language models. Our pipeline splits the text into phrases, identifies phrases with a syntactically parallel structure, and detects if these parallel phrase pairs present opposing ideas by fine-tuning the German ELECTRA model, a state-of-the-art deep learning model for the German language. Furthermore, we compare the results with multilingual BERT and German BERT. Our novel approach outperforms the state-of-the-art methods (F1-score of 50.43 %) for antithesis detection by achieving an F1-score of 65.11 %.</abstract>
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%0 Conference Proceedings
%T Using Pre-Trained Language Models in an End-to-End Pipeline for Antithesis Detection
%A Kühn, Ramona
%A Saadi, Khouloud
%A Mitrović, Jelena
%A Granitzer, Michael
%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 kuhn-etal-2024-using-pre
%X Rhetorical figures play an important role in influencing readers and listeners. Some of these word constructs that deviate from the usual language structure are known to be persuasive – antithesis is one of them. This figure combines parallel phrases with opposite ideas or words to highlight a contradiction. By identifying this figure, persuasive actors can be better identified. For this task, we create an annotated German dataset for antithesis detection. The dataset consists of posts from a Telegram channel criticizing the COVID-19 politics in Germany. Furthermore, we propose a three-block pipeline approach to detect the figure antithesis using large language models. Our pipeline splits the text into phrases, identifies phrases with a syntactically parallel structure, and detects if these parallel phrase pairs present opposing ideas by fine-tuning the German ELECTRA model, a state-of-the-art deep learning model for the German language. Furthermore, we compare the results with multilingual BERT and German BERT. Our novel approach outperforms the state-of-the-art methods (F1-score of 50.43 %) for antithesis detection by achieving an F1-score of 65.11 %.
%U https://aclanthology.org/2024.lrec-main.1502
%P 17310-17320
Markdown (Informal)
[Using Pre-Trained Language Models in an End-to-End Pipeline for Antithesis Detection](https://aclanthology.org/2024.lrec-main.1502) (Kühn et al., LREC-COLING 2024)
ACL