Data Augmentation with Translation for Turkish Irony Detection T rk e Ironi Tespiti i in eviri ile Veri ogaltimi

Date

2025-01-01

Author

Kara, Burak Kemal
Karagöz, Pınar

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Irony detection in Turkish texts is a challenging task due to the nuanced nature of irony in natural language. The limited availability of irony data for Turkish is also an important limitation to train models. In this study, the effect of data augmentation with translation for irony detection is investigated, and new models are proposed using ensemble and meta-learning approaches, in order to increase the detection performance. In the first stage, performance of classical machine learning and transformer-based models is investigated with the augmented dataset. Subsequently, ensemble and meta-learning models are constructed with variations of the transformer-based model trained on different data subsets. The created models are evaluated under cross-validation, and the performance of each classifier is measured by accuracy, precision, recall, and F1 score. The results show that the ironic sentences obtained with translation do not have exactly the same structure and quality as the original ones. However, proposed ensemble and meta learning-based models trained with the augmented data improve the irony detection performance and carry potential to be used.

Subject Keywords

data augmentation, ensemble learning, Irony detection, machine learning, meta-learning, sentence vectors, Turkish natural language processing, word vectors

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105015435133&origin=inward
https://hdl.handle.net/11511/115871

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Department of Computer Engineering, Conference / Seminar