@inproceedings{kuhn-etal-2024-using,
    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 {\%}.",
}

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 {\%}.
K{\"u}hn, Ramona Saadi, Khouloud Mitrovi{\'c}, Jelena Granitzer, Michael
Using Pre-Trained Language Models in an End-to-End Pipeline for Antithesis Detection