ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD

• DOI: 10.26615/978-954-452-072-4_005
• Publication Date: 2021-11-06T19:54:26Z
• Authors: Moustafa Al-Hajj; Mustafa Jarrar
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Computation and Language; Artificial Intelligence (cs.AI); Computer Science - Artificial Intelligence; 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; Computation and Language (cs.CL); Information Retrieval (cs.IR); Computer Science - Information Retrieval; Machine Learning (cs.LG)

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.