arXiv:2605.09156v2 Announce Type: replace Abstract: The diachronic evolution from Latin to the Romance languages involved a restructuring of the grammatical gender system from a tripartite configuration (masculine, feminine, neuter) to a bipartite one (masculine, feminine) in most Romance languages. In this work, we introduce an interpretable deep learning framework to investigate this phenomenon at both lexical and contextual levels. First, we show that conventional tokenization strategies are insufficiently robust for this low-resource historical setting, and that our proposed tokenizer impr
The paper was published as part of the ongoing academic research cycle in computational linguistics and AI applications to historical language studies, reflecting incremental advancements.
A strategic reader focused on current geopolitical, economic, or technological shifts would find this specific academic paper of minimal direct importance.
This paper offers a new interpretable deep learning framework for historical linguistic analysis, incrementally improving academic research methods without altering broader trends.
- · Computational linguists
- · Historical language researchers
Improved academic tools for analyzing linguistic evolution with deep learning.
Potentially more accurate models for reconstructing historical language features.
Deeper understanding of language change mechanisms, aiding in the development of more robust general-purpose AI for diverse linguistic tasks.
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