MISSION at KSAA - CAD 2024: A ra T 5 with A rabic Reverse Dictionary (2024)

Abstract: This research paper presents our approach for the KSAA-CAD 2024 competition, focusing on Arabic Reverse Dictionary (RD) task (Alshammari et al., 2024). Leveraging the functionalities of the Arabic Reverse Dictionary, our system allows users to input glosses and retrieve corresponding words. We provide all associated notebooks and developed models on GitHub and Hugging face, respectively. Our task entails working with a dataset comprising dictionary data and word embedding vectors, utilizing three different architectures of contextualized word embeddings: AraELECTRA, AraBERTv2, and camelBERT-MSA. We fine-tune the AraT5v2-base-1024 model for predicting each embedding, considering various hyperparameters for training and validation. Evaluation metrics include ranking accuracy, mean squared error (MSE), and cosine similarity. The results demonstrate the effectiveness of our approach on both development and test datasets, showcasing promising performance across different embedding types.

## introduction this research paper is prepared for ksaa-cad 2024 @xcite and describes our approach foot_0 as part of our contribution to this competition. all associated notebooks can be accessed on github foot_1 , and the developed models are available on hugging face foot_2 . this task focuses on arabic reverse dictionary word and gloss to word embedding, leveraging the arabic reverse dictionary's functionality. it enables users to input glosses and receive corresponding words for better comprehension. for instance, if you forget a word, you can describe it, and the model will provide either the exact word or the closest synonym. this process facilitates gloss to word embedding. the task involves working with a dataset comprising dictionary data and word embedding vectors, utilizing three different architectures of contextualized word embeddings. the dataset includes three dictionaries of contemporary arabic language: contemporary arabic language dictionary" by ahmed mokhtar omar @xcite , "mu'jam arriyadh" dictionary of arabic contemporary language @xcite , "al wassit lmf arabic dictionary" (namly, 2015) @xcite . these dictionaries conform to the iso lexical markup framework (lmf) standard @xcite and are based on lemmas, including glosses, part of speech (pos) tags, and examples. previous experiments indicated that fixed word embeddings like word2vec did not perform well, prompting a shift to contextualized word embeddings using advanced models such as electra @xcite , bert @xcite , araelec-tra @xcite , arabertv2(antoun et al.), and camelbert-msa @xcite . araelectra, based on the electra framework @xcite , trains a discriminator model, while arabertv2 uses farasa segmentation @xcite and camelbert-msa is pretrained on modern standard arabic (msa) corpora, built on the bert architecture @xcite . ## datasets a simplified version of the input data is shown in the statistics of the dataset are summarized in ## metrcies the model evaluation process follows a hierarchical approach using multiple metrics. ## result the results are as shown in ## discussion ## conclusion the study highlights the varying strengths and weaknesses of different arabic embeddings predicted using the arat5-base-1024 model. by understanding these differences, practitioners can make more informed choices about which embedding model to use for specific nlp tasks.