C her at KSAA - CAD 2024: Compressing Words and Definitions into the Same Space for A rabic Reverse Dictionary (2024)

Abstract: We present Team Cher’s submission to the ArabicNLP 2024 KSAA-CAD shared task on the reverse dictionary for Arabic—the retrieval of words using definitions as a query. Our approach is based on a multi-task learning framework that jointly learns reverse dictionary, definition generation, and reconstruction tasks. This work explores different tokenization strategies and compares retrieval performance for each embedding architecture. Evaluation using the KSAA-CAD benchmark demonstrates the effectiveness of our multi-task approach and provides insights into the reverse dictionary task for Arabic. It is worth highlighting that we achieve strong performance without using any external resources in addition to the provided training data.

## introduction reverse dictionary represents a unique and valuable technique enabling users to search for a word based on meaning or concepts @xcite . this is an inverse process to the conventional use of dictionaries: looking up definitions given a word. systems with the reverse dictionary capability hold significant potential for applications like writing assistance, information retrieval, and language education. 1 however, despite the growing interest in natural language processing (nlp) for the arabic language, reverse dictionary capabilities remain relatively under-explored compared to their english counterparts. the ksaa-cad 2024 shared task @xcite , rooted from ksaa 2023 @xcite and codwoe 2022 @xcite shared tasks, aims to advance research on "cher" means "beloved" or "precious" in french, reflecting our cherished bond with languages. it also echoes the chinese idiom "三人行，必有我师" (in three companions, one is my teacher), implying that there is always something to learn from those around us. having experimented with static embeddings such as character-level autoencoding (char), skipgram negative sampling (sgns, one variant of word2vec), and dynamic embeddings like electra @xcite , we take this opportunity to extend our method to two new arabic embedding sub-tracks: bertmsa from camelbert @xcite and bertseg from arabert @xcite . we describe our methodologies and findings in developing an arabic reverse dictionary system, hoping to contribute to the advancement of computational linguistics research for the arabic language. further, all our experiments are data-constrained-without using extra resources like pre-trained embeddings-making our method language-agnostic and generalizable. ## task setup we provide a description of the dictionary and embedding data as well as the evaluation protocol provided by the shared task organisers in this section. the details can also be found available online. 3 ## conclusion we have presented a study of arabic reverse dictionary using a joint word-definition modelling approach. we concentrated on the investigation of tokenizers and representation learning in the shared space for words and definitions. we hope our modelling approach can contribute to research on word and sentence semantics.