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    <titleInfo>
        <title>Context-Gloss Augmentation for Improving Arabic Target Sense Verification</title>
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    <name type="personal">
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            <namePart type="given">Alexandre</namePart>
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    <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).</abstract>
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            <start>254</start>
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%0 Conference Proceedings
%T Context-Gloss Augmentation for Improving Arabic Target Sense Verification
%A Malaysha, Sanad
%A Jarrar, Mustafa
%A Khalilia, Mohammed
%Y Rigau, German
%Y Bond, Francis
%Y Rademaker, Alexandre
%S Proceedings of the 12th Global Wordnet Conference
%D 2023
%8 January
%I Global Wordnet Association
%C University of the Basque Country, Donostia - San Sebastian, Basque Country
%F malaysha-etal-2023-context
%X 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).
%U https://aclanthology.org/2023.gwc-1.31
%P 254-262

Markdown (Informal)

[Context-Gloss Augmentation for Improving Arabic Target Sense Verification](https://aclanthology.org/2023.gwc-1.31) (Malaysha et al., GWC 2023)

• Context-Gloss Augmentation for Improving Arabic Target Sense Verification (Malaysha et al., GWC 2023)

ACL

• Sanad Malaysha, Mustafa Jarrar, and Mohammed Khalilia. 2023. Context-Gloss Augmentation for Improving Arabic Target Sense Verification. In Proceedings of the 12th Global Wordnet Conference, pages 254–262, University of the Basque Country, Donostia - San Sebastian, Basque Country. Global Wordnet Association.