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

arXiv: https://arxiv.org/abs/2310.19034 · HTML: https://mlsanity.com/paper/2310.19034/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 50 references that are in the map.
- First posted on arXiv 29 Oct 2023.

## Builds on (most cited first, 18 of 18)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Intent Detection with Dual Sentence Encoders](https://mlsanity.com/paper/2003.04807/) (2020)
- [Snips Voice Platform: an embedded Spoken Language Understanding system for private-by-design voice interfaces](https://mlsanity.com/paper/1805.10190/) (2018)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic](https://mlsanity.com/paper/2101.01785/) (2020)
- [The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models](https://mlsanity.com/paper/2103.06678/) (2021)
- [A Panoramic Survey of Natural Language Processing in the Arab World](https://mlsanity.com/paper/2011.12631/) (2020)
- [1.5 billion words Arabic Corpus](https://mlsanity.com/paper/1611.04033/) (2016)
- [Wojood: Nested Arabic Named Entity Corpus and Recognition using BERT](https://mlsanity.com/paper/2205.09651/) (2022)
- [Curras + Baladi: Towards a Levantine Corpus](https://mlsanity.com/paper/2205.09692/) (2022)
- [The Arabic Ontology -- An Arabic Wordnet with Ontologically Clean Content](https://mlsanity.com/paper/2205.09664/) (2022)
- [ArabGlossBERT: Fine-Tuning BERT on Context-Gloss Pairs for WSD](https://mlsanity.com/paper/2205.09685/) (2022)
- [Extracting Synonyms from Bilingual Dictionaries](https://mlsanity.com/paper/2012.00600/) (2020)
- [Nabra: Syrian Arabic Dialects with Morphological Annotations](https://mlsanity.com/paper/2310.17315/) (2023)
- [A Benchmark and Scoring Algorithm for Enriching Arabic Synonyms](https://mlsanity.com/paper/2302.02232/) (2023)
- [Lisan: Yemeni, Iraqi, Libyan, and Sudanese Arabic Dialect Copora with Morphological Annotations](https://mlsanity.com/paper/2212.06468/) (2022)
- [Arabic Fine-Grained Entity Recognition](https://mlsanity.com/paper/2310.17333/) (2023)
- [Current Trends and Approaches in Synonyms Extraction: Potential Adaptation to Arabic](https://mlsanity.com/paper/2205.10412/) (2022)

## Built on by (most cited first, 8 of 8)

- [AraFinNLP 2024: The First Arabic Financial NLP Shared Task](https://mlsanity.com/paper/2407.09818/) (2024)
- [NLU-STR at SemEval-2024 Task 1: Generative-based Augmentation and Encoder-based Scoring for Semantic Textual Relatedness](https://mlsanity.com/paper/2405.00659/) (2024)
- [ArabicNLU 2024: The First Arabic Natural Language Understanding Shared Task](https://mlsanity.com/paper/2407.20663/) (2024)
- [DarijaBanking: A New Resource for Overcoming Language Barriers in Banking Intent Detection for Moroccan Arabic Speakers](https://mlsanity.com/paper/2405.16482/) (2024)
- [AlexandriaX 2026: The First Shared Task on Dialectal Arabic Machine Translation](https://mlsanity.com/paper/2609.22796/) (2026)
- [dzFinNlp at AraFinNLP: Improving Intent Detection in Financial Conversational Agents](https://mlsanity.com/paper/2407.13565/) (2024)
- [SinaTools: Open Source Toolkit for Arabic Natural Language Processing](https://mlsanity.com/paper/2411.01523/) (2024)
- [WolBanking77: Wolof Banking Speech Intent Classification Dataset](https://mlsanity.com/paper/2509.19271/) (2025)

## Abstract (arXiv)

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.