Year: 2023
Type: conference-paper
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).
Source: Global WordNet Conference (GWC)
Authors: Sanad Malaysha, Mustafa Jarrar, Mohammed Khalilia
Institution: Birzeit University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7103892717