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ACL Findings2022

Mukayese: Turkish NLP Strikes Back

Ali Safaya, Emirhan Kurtulus, Arda Göktogan, Deniz Yüret

Abstract

Having sufficient resources for language X lifts it from the under-resourced languages class, but not necessarily from the under-researched class. In this paper, we address the problem of the absence of organized benchmarks in the Turkish language. We demonstrate that languages such as Turkish are left behind the stateof-the-art in NLP applications. As a solution, we present MUKAYESE, a set of NLP benchmarks for the Turkish language that contains several NLP tasks. We work on one or more datasets for each benchmark and present two or more baselines. Moreover, we present four new benchmarking datasets in Turkish for language modeling, sentence segmentation, and spell checking. All datasets and baselines are available under: https://github.com/ alisafaya/mukayese

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Cited by top-tier papers1

  1. TUMLU: A Unified and Native Language Understanding Benchmark for Turkic LanguagesJafar Isbarov, Arofat Akhundjanova, Mammad Hajili, Kavsar Huseynova et al.ACL 2025

Builds on7

  1. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionMike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad et al.ACL 2020 · 1,224 citations
  2. XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual GeneralisationJunjie Hu, Sebastian Ruder, Aditya Siddhant, Graham Neubig et al.ICML 2020 · 1,132 citations
  3. The State and Fate of Linguistic Diversity and Inclusion in the NLP WorldPratik Joshi, Sebastin Santy, Amar Budhiraja, Kalika Bali et al.ACL 2020 · 40 citations
  4. When Attention Meets Fast Recurrence: Training Language Models with Reduced ComputeTao LeiEMNLP 2021 · 28 citations
  5. KLEJ: Comprehensive Benchmark for Polish Language UnderstandingPiotr Rybak, Robert Mroczkowski, Janusz Tracz, Ireneusz GawlikACL 2020 · 4 citations

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