mlsanityData to 7 Oct 2026

Two-stage Pipeline for Multilingual Dialect Detection

arXiv 2303.03487 ↗First posted 6 Mar 2023cs.CL cs.AIcited by 3

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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).

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 35 references that are in the map. First posted on arXiv 6 Mar 2023.

Builds on

8 of 35 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  4. 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
  5. 2022BERTIN: Efficient Pre-Training of a Spanish Language Model using Perplexity Samplingcited by 30
  6. 2023Language Variety Identification with True Labelscited by 12
  7. 2020Transformer-based Arabic Dialect Identificationcited by 5
  8. 2017LIDE: Language Identification from Text Documentscited by 3

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Citing papers per year: 2023: 1; 2024: 1; 2025: 1.

3 papers.