  A Reverse Dictionary is a tool enabling users to discover a word based on its
provided definition, meaning, or description. Such a technique proves valuable
in various scenarios, aiding language learners who possess a description of a
word 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 winning
solution for the Arabic Reverse Dictionary shared task. This task focuses on
deriving a vector representation of an Arabic word from its accompanying
description. The shared task encompasses two distinct subtasks: the first
involves an Arabic definition as input, while the second employs an English
definition. For the first subtask, our approach relies on an ensemble of
finetuned Arabic BERT-based models, predicting the word embedding for a given
definition. The final representation is obtained through averaging the output
embeddings from each model within the ensemble. In contrast, the most effective
solution for the second subtask involves translating the English test
definitions into Arabic and applying them to the finetuned models originally
trained for the first subtask. This straightforward method achieves the highest
score across both subtasks.
