Transfer Runs Through the Lexicon: Sentence Embeddings for Languages Without Bitext

Multilingual sentence encoders are trained on parallel text, which most of the languages of the world do not have. Nevertheless, many of these languages do have dictionaries. We study a sentence encoder whose only language-specific component is a lexicon, which can be built by hand with native speakers or collected directly from dictionary entries. A student Transformer is distilled from a pretrained sentence embedding model (SONAR) on English text alone. It represents words using their frequency plus a static word embedding derived from a multilingual concept space. We build that space by propagating ConceptNet vectors over the PanLex translation graph, extended with dictionary entries harvested by an LLM. A new language is added by swapping these static word embeddings via lookup tables, with no training. For languages whose lexicon covers at least 85% of running words it reaches 99.1% xsim and 53.7% xSIM++. On all 216 FLORES+ devtest languages, many of them with small lexicons, the model still reaches 60.8% xsim and 23.9% xSIM++, against 21.9% and 10.1% for a training-free baseline that averages the same concept vectors. A key result of this work is an empirical coverage law: two statistics available before any training explain $R^2=0.85$ of the cross-language variance in xSIM++. Deleting known words at random, an intervention that holds the language, the sentences and the encoder fixed, reproduces about four fifths of the slope of the law. Coverage also keeps a partial correlation of 0.90 with quality after controlling for relatedness to high-resource languages (0.30 for relatedness). Finally, we confirm that xsim is saturated for this scenario: it overstates xSIM++ by 36.9 points on average, and it barely registers a 12.8-point xSIM++ difference between two encoders built to differ.