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.

在本研究中，我们提出了一种面向低资源场景的对齐方法：MAML-Align，利用基于优化的模型无关元学习器 MAML 进行元蒸馏学习，从面向单语和双语语义搜索的 Teacher 元迁移模型 T-MAML 中提取知识，再将其转移到面向多语言语义搜索的 Student 模型 S-MAML 中。我们的实证结果表明，相对于朴素微调方法，我们的元蒸馏方法不仅提升了基于句子转换器的强基线的效果，而且显著超过了 MAML 所提供的增益。此外，多语言元蒸馏学习还提高了对未知语言的泛化能力。