Lexical Intent Recognition in Urdu Queries Using Deep Neural Networks

Shams, Sana; Aslam, Muhammad; Martinez-Enriquez, A. M.

doi:10.1007/978-3-030-33749-0_4

Cited by 3 publications

(1 citation statement)

References 21 publications

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“…Other related languages for which intent detection was studied is Urdu. In (Shams et al, 2019), the authors translated the Air Travel Information System (ATIS) (Hemphill et al, 1990) and AOL datasets from English to Urdu and performed intent detection using a combination of CNNs, LSTMs and BiLSTMs models. For ATIS, CNN performed the best at 0.924 accuracy, while for AOL, BiLSTM achieved the highest performance at 0.831 accuracy.…”

Section: Related Workmentioning

confidence: 99%

ArBanking77: Intent Detection Neural Model and a New Dataset in Modern and Dialectical Arabic

Jarrar,

Birim,

Khalilia

et al. 2023

Proceedings of ArabicNLP 2023

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This paper presents the ArBanking77, a large Arabic dataset for intent detection in the banking domain. Our dataset was arabized and localized from the original English Banking77 dataset, which consists of 13,083 queries to ArBanking77 dataset with 31,404 queries in both Modern Standard Arabic (MSA) and Palestinian dialect, with each query classified into one of the 77 classes (intents). Furthermore, we present a neural model, based on AraBERT, fine-tuned on ArBanking77, which achieved an F1-score of 0.9209 and 0.8995 on MSA and Palestinian dialect, respectively. We performed extensive experimentation in which we simulated low-resource settings, where the model is trained on a subset of the data and augmented with noisy queries to simulate colloquial terms, mistakes and misspellings found in real NLP systems, especially live chat queries. The data and the models are publicly available at https://sina.birzeit.edu/arbanking77.

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Section: Related Workmentioning

confidence: 99%

ArBanking77: Intent Detection Neural Model and a New Dataset in Modern and Dialectical Arabic

Jarrar,

Birim,

Khalilia

et al. 2023

Proceedings of ArabicNLP 2023

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DarijaBanking: A new resource for overcoming language barriers in banking intent detection for Moroccan Arabic speakers

Skiredj,

Azhari,

Berrada

et al. 2024

Nat. lang. processing

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Navigating the complexities of language diversity is a central challenge in developing robust natural language processing systems, especially in specialized domains like banking. The Moroccan Dialect of Arabic (Darija) serves as a common language that blends cultural complexities, historical impacts, and regional differences, which presents unique challenges for language models due to its divergence from Modern Standard Arabic and influence from French, Spanish, and Tamazight. To tackle these challenges, this paper introduces Darija Banking, a novel Darija dataset aimed at enhancing intent classification in the banking domain. DarijaBanking comprises over 1800 parallel high-quality queries in Darija, Modern Standard Arabic (MSA), English, and French, organized into 24 intent classes. We experimented various intent classification methods, including full fine-tuning of monolingual and multilingual models, zero-shot learning, retrieval-based approaches, and Large Language Model prompting. Furthermore, we propose BERTouch, a BERT-based language model fine-tuned on intent detection in Darija, which outperforms state-of-the-art models, including OpenAI’s GPT-4, achieving F1 scores of 0,98 and 0,96 on both Darija and MSA, respectively. The results provide insights into enhancing Moroccan Darija banking intent detection systems, highlighting the value of domain-specific data annotation and balancing precision and cost-effectiveness.

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