Do Multilingual Encoders Produce Language-Consistent Semantic IDs?

Title:Do Multilingual Encoders Produce Language-Consistent Semantic IDs?

Abstract:Semantic IDs (SIDs) compress item embeddings into discrete code sequences used in generative retrieval. We ask whether a multilingual encoder is sufficient for different-language renderings of the same product to receive language-consistent SIDs. Using Amazon ESCI listings rendered in English, Spanish, and Japanese, we test whether translations remain close to their English source, whether residual quantization is unusually sensitive to translation-induced movement, and whether multilingual or language-balanced quantizer fitting improves SID agreement. Multilingual E5 places translations measurably apart: under an English-heavy fit, a Japanese translation preserves the first SID code of its English counterpart in only 7.7% of cases, compared with 89.0% for an English rewording. Distance-matched product-directed controls produce nearly the same full-SID mismatch as translation, providing no evidence that the quantizer selectively amplifies language directions. Balancing the fitting mixture makes codebook use more uniform but further reduces cross-lingual prefix agreement: Spanish first-code consistency falls from 28.3% to 6.6%, while an English-only fit preserves it for 67.6% of Spanish translations. These results show that multilingual exposure and balanced codebook use alone do not guarantee language-consistent SIDs.

7 pages, 8 tables. Accepted as a short paper at WiNLP 2026, co-located with EMNLP 2026

Information Retrieval (cs.IR); Computation and Language (cs.CL)

arXiv:2610.01139 [cs.IR]

(or arXiv:2610.01139v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2610.01139 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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