Mind Your Moras: Orthography-Aware Error Analysis of Neural Japanese Morphological Generation
arXiv:2605.20043v2 Announce Type: replace Abstract: We present an orthography-aware error analysis of Japanese past-tense morphological inflection, treating hiragana not merely as a transcriptional medium, but as a representational system encoding morphophonological distinctions that may influence model generalization. We evaluate two character-level sequence-to-sequence architectures on past-tense formation using datasets formatted according to the SIGMORPHON 2020 and 2023 shared task conventions. Despite high aggregate accuracy, models exhibit systematic, linguistically interpretable errors
The paper uses recent SIGMORPHON shared task conventions, indicating progress in computational linguistics and AI's ability to handle complex morphological systems.
Sophisticated error analysis in AI models reveals systematic linguistic challenges, which is crucial for developing more robust and culturally nuanced AI systems, particularly in language processing.
The understanding of AI model generalization in complex linguistic tasks is refined, moving beyond aggregate accuracy to systematic, linguistically-interpretable errors.
- · Computational linguists
- · AI language model developers (non-English)
- · Natural Language Processing (NLP) researchers
- · Developers relying solely on superficial accuracy metrics
- · Generic AI translation services without deep linguistic understanding
Improved understanding of AI limitations in nuanced language tasks.
Development of more sophisticated model architectures tailored to specific linguistic challenges.
Enhanced cross-lingual AI capabilities leading to better human-computer interaction in diverse languages.
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