2024/10/31 by Iñigo Parra, Parra, Iñigo
Computer Science · Engineering · #Advanced Computational Techniques and Applications #Advanced Research in Systems and Signal Processing #Computation and Language (cs.CL) #FOS: Computer and information sciences #Industrial Technology and Control Systems
paper · pdf · doi:10.48550/arxiv.2410.23656
openalex publication_date 2024/10/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study investigates the impact of morphological typology on tokenization and language modeling performance. We focus on languages with synthetic and analytical morphological structures and examine their productivity when tokenized using the byte-pair encoding (BPE) algorithm. We compare the performance of models trained with similar amounts of data in different languages. Our experiments reveal that languages with synthetic features exhibit greater subword regularity and productivity with BPE tokenization and achieve better results in language modeling tasks. We also observe that the typological continuum from linguistic theory is reflected in several experiments. These findings suggest a correlation between morphological typology and BPE tokenization efficiency.