Mukayese: Turkish NLP Strikes Back
This addresses the problem of under-researched languages like Turkish for NLP researchers and practitioners, though it is incremental as it focuses on creating benchmarks rather than novel methods.
The paper tackles the lack of organized benchmarks in Turkish NLP by introducing Mukayese, a set of benchmarks with datasets and baselines for tasks like language modeling, sentence segmentation, and spell checking, demonstrating that Turkish lags behind state-of-the-art NLP applications.
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 state-of-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