Rosetta Stone at KSAA-RD Shared Task: A Hop From Language Modeling To Word--Definition Alignment
This work provides a tool for language learners and writers to find words from descriptions, addressing the Tip-of-the-Tongue phenomenon, but it is incremental as it applies existing methods to a new task.
The authors tackled the Arabic Reverse Dictionary shared task by developing an ensemble of finetuned BERT models to predict word embeddings from definitions, achieving the highest score across both Arabic and English definition subtasks.
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.