@inproceedings{malaysha-etal-2023-context,
    title = "Context-Gloss Augmentation for Improving {A}rabic Target Sense Verification",
    author = "Malaysha, Sanad  and
      Jarrar, Mustafa  and
      Khalilia, Mohammed",
    editor = "Rigau, German  and
      Bond, Francis  and
      Rademaker, Alexandre",
    booktitle = "Proceedings of the 12th Global Wordnet Conference",
    month = jan,
    year = "2023",
    address = "University of the Basque Country, Donostia - San Sebastian, Basque Country",
    publisher = "Global Wordnet Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2023.gwc-1.31/",
    pages = "254--262",
    abstract = "Arabic language lacks semantic datasets and sense inventories. The most common semantically-labeled dataset for Arabic is the ArabGlossBERT, a relatively small dataset that consists of 167K context-gloss pairs (about 60K positive and 107K negative pairs), collected from Arabic dictionaries. This paper presents an enrichment to the ArabGlossBERT dataset, by augmenting it using (Arabic-English-Arabic) machine back-translation. Augmentation increased the dataset size to 352K pairs (149K positive and 203K negative pairs). We measure the impact of augmentation using different data configurations to fine-tune BERT on target sense verification (TSV) task. Overall, the accuracy ranges between 78{\%} to 84{\%} for different data configurations. Although our approach performed at par with the baseline, we did observe some improvements for some POS tags in some experiments. Furthermore, our fine-tuned models are trained on a larger dataset covering larger vocabulary and contexts. We provide an in-depth analysis of the accuracy for each part-of-speech (POS)."
}