Generalization of lexical representations to downstream historical-linguistic and NLP tasks

Determine whether the lexical representations learned by the Dual Contrastive Word Encoder generalize well to downstream tasks such as cognate discovery, borrowing and contact detection, language clustering, dialectometry, and transfer-language selection for low-resource natural language processing without further adaptation.

Background

The paper presents DualCWE as a self-supervised model that learns lexical representations from raw multilingual IPA wordlists and demonstrates their utility for large-scale phylogenetic inference and concept-stability estimation. The discussion identifies several additional applications, including cognate discovery, borrowing and contact detection, language clustering, dialectometry, and transfer-language selection for low-resource NLP. Although the representations appear to encode historically relevant patterns, the paper does not establish whether they transfer effectively to these tasks without task-specific adaptation. The authors therefore leave this generalization question unresolved for future work.