Author

Michelle Wastl

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Preprint Aug 2026

Scaling Unsupervised Word Alignment to Documents via Structural Constraints

CTFAlign is introduced, a lightweight, training-free approach for document-level word alignment that applies a coarse-to-fine refinement strategy that restricts the alignment search space to semantically similar regions and introduces MDPAlign, a simpler alternative that constrains alignments by position with a main diagonal prior.

Michelle Wastl, Jannis Vamvas, Rico Sennrich · 0 citations

#natural language process... Preprint Jun 2026

ALEE: Any-Language Evaluation of Embeddings via English-Centric Minimal Pairs

A large-scale empirical study across a diverse set of embedding models and 275+ languages spanning three parallel datasets, exposing persistent gaps in cross-lingual semantic representation that track language prevalence in training resources and subword tokenization.

Andrianos Michail, Stylianos Psychias, Michelle Wastl et al. · 0 citations