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

Abstract:Text embeddings are standard for semantic similarity tasks, yet their evaluation remains an open challenge. Current benchmarks are static, cover only a limited set of languages, are often domain-specific, susceptible to overfitting, and poorly representative of low-resource languages. To address these limitations, we introduce ALEE, a framework that extends Sentence Smith (Li et al., 2025) to the cross-lingual and paragraph level. ALEE uses Abstract Meaning Representations (AMR) to generate English minimal pairs with controlled, fine-grained semantic shifts, which are paired with translations in target languages. This approach enables targeted diagnostics for models in any language with English parallel data. We conduct a large-scale empirical study across a diverse set of embedding models and 275+ languages spanning three parallel datasets. On ALEE, performance varies substantially across languages, text lengths, and linguistic phenomena, exposing persistent gaps in cross-lingual semantic representation that track language prevalence in training resources and subword tokenization. We release ALEE at this https URL

Computation and Language (cs.CL)

arXiv:2607.00171 [cs.CL]

(or arXiv:2607.00171v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.00171 Focus to learn more arXiv-issued DOI via DataCite

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