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).- Anthology ID:
- 2023.gwc-1.31
- Volume:
- Proceedings of the 12th Global Wordnet Conference
- Month:
- January
- Year:
- 2023
- Address:
- University of the Basque Country, Donostia - San Sebastian, Basque Country
- Editors:
- German Rigau, Francis Bond, Alexandre Rademaker
- Venue:
- GWC
- SIG:
- Publisher:
- Global Wordnet Association
- Note:
- Pages:
- 254–262
- Language:
- URL:
- https://aclanthology.org/2023.gwc-1.31
- DOI:
- Cite (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.
- Cite (Informal):
- Context-Gloss Augmentation for Improving Arabic Target Sense Verification (Malaysha et al., GWC 2023)
- PDF:
- https://preview.aclanthology.org/revert-3132-ingestion-checklist/2023.gwc-1.31.pdf