rematchka at ArabicNLU2024: Evaluating Large Language Models for Arabic Word Sense and Location Sense Disambiguation

AbstractNatural Language Understanding (NLU) plays a vital role in Natural Language Processing (NLP) by facilitating semantic interactions. Arabic, with its diverse morphology, poses a challenge as it allows multiple interpretations of words, leading to potential misunderstandings and errors in NLP applications. In this paper, we present our approach for tackling Arabic NLU shared tasks for word sense disambiguation (WSD) and location mention disambiguation (LMD). Various approaches have been investigated from zero-shot inference of large language models (LLMs) to fine-tuning of pre-trained language models (PLMs). The best approach achieved 57% on WSD task ranking third place, while for the LMD task, our best systems achieved 94% MRR@1 ranking first place.