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 2 publications

(1 citation statement)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Related Workmentioning

confidence: 99%