Enhancing Dialectal A rabic Intent Detection through Cross-Dialect Multilingual Input Augmentation (2025)

Abstract: Addressing the challenges of Arabic intent detection amid extensive dialectal variation, this study presents a crossdialtectal, multilingual approach for classifying intents in banking and migration contexts. By augmenting dialectal inputs with Modern Standard Arabic (MSA) and English translations, our method leverages cross-lingual context to improve classification accuracy. We evaluate single-input (dialect-only), dual-input (dialect + MSA), and triple-input (dialect + MSA + English) models, applying language-specific tokenization for each. Results demonstrate that, in the migration dataset, our model achieved an accuracy gain of over 50% on Tunisian dialect, increasing from 43.3% with dialect-only input to 94% with the full multilingual setup. Similarly, in the PAL (Palestinian dialect) dataset, accuracy improved from 87.7% to 93.5% with translation augmentation, reflecting a gain of 5.8 percentage points. These findings underscore the effectiveness of our approach for intent detection across various Arabic dialects.

## introduction natural language understanding (nlu) powers the smart applications we use daily by helping machines grasp human intent. yet, intent detection in arabic is especially challenging due to the language's diversity. spoken by over 400 million people across 22+ countries, arabic includes both modern standard arabic (msa) for formal use and a variety of regional dialects (al' ammiya) for everyday speech. each dialect presents unique challenges, from vocabulary and grammar to pronunciation, making intent detection a complex task. this linguistic diversity poses a significant challenge for nlu systems. for example, the phrase "illegal migration" translates to this study introduces a novel approach that combines multilingual and multidialectal strategies to detect intent in banking (pal dataset) and migration contexts (gpt-generated migration dataset). each dialectal text is translated into msa and english to see if the structural clarity of msa and the broader context of english enhance intent recognition. msa adds consistency, while english captures additional meaning that might otherwise be missed. ## related work multidialectal intent detection in arabic presents unique challenges due to the diverse dialects and limited annotated data. early research predominantly focused on msa, but the advent of pretrained models like bert opened new avenues for arabic nlp. francony et al. 2019 addressed the issue of dialect diversity by proposing a hierarchical deep learning framework. their two-step model distinguishes msa from dialects and further classifies dialects by region, offering a structured foundation for tasks like intent detection. similarly, shammary et al. 2022 explored a comparative analysis of traditional tf-idf approaches and transformer-based models for the nadi 2022 shared task. their findings demonstrated that while transformers are powerful, tf-idf can be a competitive and lightweight alternative for lowresource dialects, emphasizing the value of efficient methods in resource-constrained settings. al hariri and abu farha 2024 showed that arabic bert models, while effective for msa, required additional fine-tuning for dialectal arabic. to address this, elkordi et al. 2024 introduced contrastive learning techniques to detect intents in different dialects, particularly in the banking sector, while ramadan et al. 2024 developed a bert-based ensemble model for the detection of cross-dialectal intent. one major challenge is data scarcity. duwairi and abushaqra 2021 addressed this issue through back-translation and paraphrasing, improving performance for low-resource dialects like moroccan and sudanese arabic. similarly, el-makky et al. 2024 explored transfer learning to fine-tune models trained on high-resource dialects and apply them to others, enhancing generalization across dialects. to further address dialect-specific challenges, skiredj et al. 2024 introduced the darijabanking dataset for moroccan arabic intent detection in the banking domain. the study presents bertouch, a darijaspecific bert model achieving state-of-the-art performance. their findings highlight the need for domain-specific resources and multilingual approaches for effective intent detection. shared tasks like arafinnlp malaysha et al. 2024a have provided benchmark datasets for multidialectal arabic nlp. these challenges have helped researchers explore advanced techniques such as pretrained models and data augmentation to enhance performance in intent detection for arabic dialects. fares and touileb 2024 fine-tuned a t5 model and generated synthetic data in moroccan, tunisian, and saudi dialects. by leveraging model ensembling, they highlighted synthetic data's role in handling dialectal variation. ## dataset our study draws on two primary datasets: the first is arbanking77 @xcite ) provided for the shared task 1 of the arafinnlp 2024 @xcite ), which contains arabic banking queries in both msa and the palestinian (pal) dialect, labeled across 77 intent categories. our analysis used only the pal subset, which focuses on the palestinian dialect. the second dataset was generated foot_0 with gpt-4, centering on tunisian dialect text related to illegal migration. this dataset is labeled by intent strength-categorized as nonintent, weak intent, or strong intent-and each entry was meticulously validated by a native tunisian speaker with specialized expertise in dialectal nuances and migration-related terminology, ensuring both linguistic fidelity and contextual depth. for our experiments, we split the pal training set into 85% for training and 15% for testing, using the dev set for validation. the migration dataset was divided into 70% training, 15% validation, and 15% testing, ensuring balanced evaluation across intent strength labels. ## methodology in this section, we detail our methodology for developing models capable of detecting intents in multi-dialectal banking and migration datasets. our approach combines translation, tokenization, and model configurations designed to harness the benefits of modern standard arabic (msa) and english alongside dialectal inputs. ## model architecture we developed three configurations: the dialect-only model (dom), the dialect-msa model (dmm) (dialect + msa), and the dialect-msa-english model (dmem) (dialect + msa + english). these configurations allow us to assess whether adding msa and english translations enhances model performance.. figure1 depicts an outline of our approach. ## experimental setup we trained models on both datasets (pal and migration) with a batch size of 8, using the adamw optimizer. the learning rate was set to 1e-5 for the migration and 3e-5 for pal, with a weight decay of 1e-4 for regularization. cross-entropy loss was used for multiclass classification. to improve generalization with limited data in the migration dataset (2000 samples), we applied layer freezing to the lower layers of the camelbert, mar-bert, and bert encoders, preserving their pretrained linguistic embeddings and focusing optimization on the task-specific upper layers. gradient clipping with a maximum norm of 1.0 was implemented to stabilize training, which is especially beneficial in a multi-encoder setup. early stopping with a patience of 2 epochs was applied, and all models were trained for 3 epochs to balance performance and computational efficiency. ## results and discusssion ## conclusion and future work our findings show that adding msa and english translations to dialectal arabic improves intent classification. however, challenges persist due to the msa bias in pre-trained models, impacting performance, particularly for the tunisian dialect. expanding training to cover more dialects could help create a more inclusive model. additionally, fine-tuning large language models on dialectal arabic holds promise. this approach may better capture linguistic and cultural nuances, enabling more accurate and adaptable intent classification across diverse arabic-speaking communities.