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Self-Supervised Lexical Representation Learning for Fast, Large-Scale Phylogenetic Inference

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

Computational phylogenetics has become an essential tool in historical linguistics, yet its application at a global scale remains constrained by two factors: the labor-intensive manual annotation of cognacy judgments required for character-based methods and the substantial computational cost of inference on large datasets. This paper introduces a fully self-supervised contrastive learning framework that learns lexical representations directly from raw IPA-transcribed wordlists, without requiring cognacy annotations, alignments, or additional expert input. The model employs a dual contrastive o

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