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A Reverse Dictionary is a tool enabling users to discover a word based on itsprovided definition, meaning, or description. Such a technique proves valuablein various scenarios, aiding language learners who possess a description of aword without its identity, and benefiting writers seeking precise terminology.These scenarios often encapsulate what is referred to as the"Tip-of-the-Tongue" (TOT) phenomena. In this work, we present our winningsolution for the Arabic Reverse Dictionary shared task. This task focuses onderiving a vector representation of an Arabic word from its accompanyingdescription. The shared task encompasses two distinct subtasks: the firstinvolves an Arabic definition as input, while the second employs an Englishdefinition. For the first subtask, our approach relies on an ensemble offinetuned Arabic BERT-based models, predicting the word embedding for a givendefinition. The final representation is obtained through averaging the outputembeddings from each model within the ensemble. In contrast, the most effectivesolution for the second subtask involves translating the English testdefinitions into Arabic and applying them to the finetuned models originallytrained for the first subtask. This straightforward method achieves the highestscore across both subtasks.

Rosetta Stone at KSAA-RD Shared Task: A Hop From Language Modeling To Word--Definition Alignment