Baleegh at KSAA - CAD 2024: Towards Enhancing A rabic Reverse Dictionaries (2024)

Abstract: The domain of reverse dictionaries (RDs), while advancing in languages like English and Chinese, remains underdeveloped for Arabic. This study attempts to explore a data-driven approach to enhance word retrieval processes in Arabic RDs. The research focuses on the ArabicNLP 2024 Shared Task, named KSAA-CAD, which provides a dictionary dataset of 39,214 word-gloss pairs, each with a corresponding target word embedding. The proposed solution aims to surpass the baseline performance by employing SOTA deep learning models and innovative data expansion techniques. The methodology involves enriching the dataset with contextually relevant examples, training a T5 model to align the words to their glosses in the space, and evaluating the results on the shared task metrics. We find that our model is closely aligned with the baseline performance on bertseg and bertmsa targets, however does not perform well on electra target, suggesting the need for further exploration.

## introduction while reverse dictionaries have witnessed advancements in languages like english and chinese e.g. wantwords @xcite , they remain less developed and explored for arabic. this gap is particularly concerning for a language with a rich linguistic heritage and widespread use. the only ongoing effort in this domain is the first arabic rd shared task launched in 2023 by king salman global academy for arabic language (ksaa) foot_0 @xcite , and the recent ksaa-cad (contemporary arabic dictionary) shared task for 2024 2 . reverse dictionaries are a form of dictionaries where a description yields a set of words from the dictionary that semantically matches the description. a prime use-case is their application in data exploration and analysis, where reverse dictionaries facilitate the identification of relevant features within complex textual datasets by generating key terms aligned with the text meaning. this enhances the efficiency of data mining and fosters the discovery of new insights @xcite . the arabic language, with its intricate morphology and diverse dialects, presents unique challenges for natural language processing (nlp) tasks. rds are crucial tools for language learners, translators, and researchers, enabling them to identify words based on their meanings or descriptions. recent studies have explored various approaches to rd. in @xcite , the authors demonstrated success as the winning solution in the 2023 shared task @xcite using an ensemble of fine-tuned bert models. their results formed the sota baseline for this 2024 shared task. in @xcite , authors focused on enhancing arabic word embeddings through a modified bert model and data augmentation while in @xcite , authors utilized a semidecoder architecture with an sbert encoder for effective word definition encoding. in other languages, authors of @xcite proposed a unique approach using mt5 for indian languages, while authors in @xcite emphasized the importance of high-quality lexicographic data for optimal rd model performance. for english, the authors in @xcite embedded both the definitions and words into the same shared space using transformer-based architectures to optimize the model across both tasks simultaneously. the model demonstrated superior performance in rd tasks, achieving high accuracy and consistency over previous methods. in this paper, the use of a rd is cast as a supervised learning task and the main questions addressed are: we leverage the new ksaa-cad dataset, which is shared exclusively with the participants of the 2024 rd shared task @xcite , comprising word-gloss pairs with corresponding target word embedding, alongside a sota baseline results. our goal is to surpass or match the baseline performance. the methodology goes through 2 steps: the code utilized in this study has been made available on github 3 to ensure the reproducibility of the experimental results. ## dataset the ksaa-cad dataset is an arabic dictionary dataset containing 39,214 entries, collected from various arabic dictionaries. ## system recent studies on rds have utilized pre-trained transformers, as seen in literature, most of which focused on bert-based transformers for arabic. consequently, the potential for exploring other architectures and pre-trained models for the arabic language remains intact. our model architecture is based on arat5 v2 foot_4 (nagoudi et al., 2021), a fine-tuned t5 transformer model for arabic. the input is processed by the arat5 v2 encoder, producing a hidden state matrix. a pooling layer 3 then converts this matrix into a fixed-length vector, which is subsequently fed into a linear layer for the final prediction. this architecture enables effective utilization of arat5 v2's language understanding capabilities for rd tasks. the input is a vector of size 256. we train 2 models for 2 epochs with the same setup but different inputs, the number of epochs being low due to the over-fitting noticed when training for higher than 2 epochs. the first model is trained on only glosses, while the other on the glosses merged with example retrieved in step 2.1. mean square error is used as a loss function, and adam as an optimizer with a learning rate value of 3e-5. ## results and discussion overall, our models did not surpass the baseline results across all embedding types. nonetheless, we achieved competitive performance, particularly in predicting bertseg and bertmsa embeddings, which indicate the model is capable of aligning the words from both spaces to glosses, suggesting ## conclusion although not exceeding the baseline on all metrics, the initial results show promise for our approach. further development could lead to exceeding baseline performance across all metrics. arat5 v2 generally performed well on the given rd task, and further experimentation could unveil more of it potential. future work could involve several enhancements to the current model. one avenue for improvement is refining the data enrichment process, potentially by incorporating diverse resources beyond wikipedia to enhance the model's knowledge base and adaptability. additionally, exploring more sophisticated architectures and fine-tuning hyperparameters could further optimize the model's performance and accuracy. finally, a promising direction would be to train the model's encoder-decoder architecture to directly predict word sequences instead of relying on target embeddings, potentially improving the generation of coherent and contextually relevant text.