Morphological segmentation of low-resource Indonesian dialects using unsupervised neural models - Muhammad Iqbal Ibrahim (Indonesian Journal of Computational Language Studies, 2026)
Background: Indonesia’s linguistic diversity, with many underrepresented dialects, poses challenges for morphological analysis under low-resource conditions. Objective: This study examines whether unsupervised neural models can learn morphological structure in Indonesian dialects without annotated data. Method: A comparative unsupervised approach was applied using probabilistic segmentation, character-level BiLSTM, and Bayesian neural models on raw dialectal corpora. Results: Probabilistic methods capture frequent roots and affixes but struggle with reduplication and clitics; character-level models better handle phonological variation, while Bayesian models achieve the most balanced performance with stronger coherence and cross-dialect generalization. Implication: These findings highlight the potential of unsupervised, probabilistic approaches to support inclusive language technology for low-resource dialects. Novelty: This study reconceptualizes dialectal morphology as a latent probabilistic system and demonstrates the effectiveness of Bayesian unsupervised neural segmentation.