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Multilingual semantic search is the task of retrieving relevant contents to aquery expressed in different language combinations. This requires a bettersemantic understanding of the user's intent and its contextual meaning.Multilingual semantic search is less explored and more challenging than itsmonolingual or bilingual counterparts, due to the lack of multilingual parallelresources for this task and the need to circumvent "language bias". In thiswork, we propose an alignment approach: MAML-Align, specifically forlow-resource scenarios. Our approach leverages meta-distillation learning basedon MAML, an optimization-based Model-Agnostic Meta-Learner. MAML-Align distillsknowledge from a Teacher meta-transfer model T-MAML, specialized intransferring from monolingual to bilingual semantic search, to a Student modelS-MAML, which meta-transfers from bilingual to multilingual semantic search. Tothe best of our knowledge, we are the first to extend meta-distillation to amultilingual search application. Our empirical results show that on top of astrong baseline based on sentence transformers, our meta-distillation approachboosts the gains provided by MAML and significantly outperforms naivefine-tuning methods. Furthermore, multilingual meta-distillation learningimproves generalization even to unseen languages.

Multilingual Sentence-Level Semantic Search using Meta-Distillation Learning