  Multilingual semantic search is the task of retrieving relevant contents to a
query expressed in different language combinations. This requires a better
semantic understanding of the user's intent and its contextual meaning.
Multilingual semantic search is less explored and more challenging than its
monolingual or bilingual counterparts, due to the lack of multilingual parallel
resources for this task and the need to circumvent "language bias". In this
work, we propose an alignment approach: MAML-Align, specifically for
low-resource scenarios. Our approach leverages meta-distillation learning based
on MAML, an optimization-based Model-Agnostic Meta-Learner. MAML-Align distills
knowledge from a Teacher meta-transfer model T-MAML, specialized in
transferring from monolingual to bilingual semantic search, to a Student model
S-MAML, which meta-transfers from bilingual to multilingual semantic search. To
the best of our knowledge, we are the first to extend meta-distillation to a
multilingual search application. Our empirical results show that on top of a
strong baseline based on sentence transformers, our meta-distillation approach
boosts the gains provided by MAML and significantly outperforms naive
fine-tuning methods. Furthermore, multilingual meta-distillation learning
improves generalization even to unseen languages.
