# Two-stage Pipeline for Multilingual Dialect Detection

arXiv: https://arxiv.org/abs/2303.03487 · HTML: https://mlsanity.com/paper/2303.03487/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 35 references that are in the map.
- First posted on arXiv 6 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Sampling](https://mlsanity.com/paper/2207.06814/) (2022)
- [Language Variety Identification with True Labels](https://mlsanity.com/paper/2303.01490/) (2023)
- [Transformer-based Arabic Dialect Identification](https://mlsanity.com/paper/2011.00699/) (2020)
- [LIDE: Language Identification from Text Documents](https://mlsanity.com/paper/1701.03682/) (2017)

## Built on by (most cited first, 3 of 3)

- [Findings of the VarDial Evaluation Campaign 2023](https://mlsanity.com/paper/2305.20080/) (2023)
- [Enhancing Portuguese Variety Identification with Cross-Domain Approaches](https://mlsanity.com/paper/2502.14394/) (2025)
- [Common Ground, Diverse Roots: The Difficulty of Classifying Common Examples in Spanish Varieties](https://mlsanity.com/paper/2412.11750/) (2024)

## Abstract (arXiv)

Dialect Identification is a crucial task for localizing various Large Language Models. This paper outlines our approach to the VarDial 2023 shared task. Here we have to identify three or two dialects from three languages each which results in a 9-way classification for Track-1 and 6-way classification for Track-2 respectively. Our proposed approach consists of a two-stage system and outperforms other participants' systems and previous works in this domain. We achieve a score of 58.54% for Track-1 and 85.61% for Track-2. Our codebase is available publicly (https://github.com/ankit-vaidya19/EACL_VarDial2023).