# Dialect Identification in Nuanced Arabic Tweets Using Farasa Segmentation and AraBERT

arXiv: https://arxiv.org/abs/2102.09749 · HTML: https://mlsanity.com/paper/2102.09749/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 19 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [AraBERT: Transformer-based Model for Arabic Language Understanding](https://mlsanity.com/paper/2003.00104/) (2020)
- [NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2010.11334/) (2020)
- [AraELECTRA: Pre-Training Text Discriminators for Arabic Language Understanding](https://mlsanity.com/paper/2012.15516/) (2020)
- [Arabic natural language processing: An overview](https://mlsanity.com/paper/1903.02784/) (2019)

## Built on by (most cited first, 1 of 1)

- [NADI 2021: The Second Nuanced Arabic Dialect Identification Shared Task](https://mlsanity.com/paper/2103.08466/) (2021)

## Abstract (arXiv)

This paper presents our approach to address the EACL WANLP-2021 Shared Task 1: Nuanced Arabic Dialect Identification (NADI). The task is aimed at developing a system that identifies the geographical location(country/province) from where an Arabic tweet in the form of modern standard Arabic or dialect comes from. We solve the task in two parts. The first part involves pre-processing the provided dataset by cleaning, adding and segmenting various parts of the text. This is followed by carrying out experiments with different versions of two Transformer based models, AraBERT and AraELECTRA. Our final approach achieved macro F1-scores of 0.216, 0.235, 0.054, and 0.043 in the four subtasks, and we were ranked second in MSA identification subtasks and fourth in DA identification subtasks.