Nullpointer at ArAIEval Shared Task: Arabic Propagandist Technique Detection with Token-to-Word Mapping in Sequence Tagging
<|reference_start|>Nullpointer at ArAIEval Shared Task: Arabic Propagandist Technique Detection with Token-to-Word Mapping in Sequence Tagging: This paper investigates the optimization of propaganda technique detection in Arabic text, including tweets \& news paragraphs, from ArAIEval shared task 1. Our approach involves fine-tuning the AraBERT v2 model with a neural network classifier for sequence tagging. Experimental results show relying on the first token of the word for technique prediction produces the best performance. In addition, incorporating genre information as a feature further enhances the model's performance. Our system achieved a score of 25.41, placing us 4$^{th}$ on the leaderboard. Subsequent post-submission improvements further raised our score to 26.68.<|reference_end|>
@article{abir2024nullpointer,
    title={Nullpointer at ArAIEval Shared Task: Arabic Propagandist Technique
  Detection with Token-to-Word Mapping in Sequence Tagging},
    author={Abrar Abir and Kemal Oflazer},
    journal={arXiv preprint arXiv:2407.01360},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.01360},
    primaryClass={cs.CL}
}