  Using pre-trained transformer models such as BERT has proven to be effective
in many NLP tasks. This paper presents our work to fine-tune BERT models for
Arabic Word Sense Disambiguation (WSD). We treated the WSD task as a
sentence-pair binary classification task. First, we constructed a dataset of
labeled Arabic context-gloss pairs (~167k pairs) we extracted from the Arabic
Ontology and the large lexicographic database available at Birzeit University.
Each pair was labeled as True or False and target words in each context were
identified and annotated. Second, we used this dataset for fine-tuning three
pre-trained Arabic BERT models. Third, we experimented the use of different
supervised signals used to emphasize target words in context. Our experiments
achieved promising results (accuracy of 84%) although we used a large set of
senses in the experiment.
