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.

翻译：多语言语义搜索是一项从不同语言组合的查询中检索相关内容的任务。这需要深入理解用户意图的语义及其语境含义。相较于单语或双语语义搜索，多语言语义搜索由于缺乏多语言并行资源且需规避"语言偏差"，相关研究尚不充分且更具挑战性。本文针对低资源场景提出一种对齐方法：MAML-Align。该方法基于优化型模型无关元学习器（MAML），采用元蒸馏学习框架。MAML-Align将教师元迁移模型T-MAML（专精于从单语到双语语义搜索的迁移）的知识蒸馏至学生模型S-MAML（实现从双语到多语言语义搜索的元迁移）。据我们所知，这是首次将元蒸馏扩展至多语言搜索应用。实验结果表明，在基于句转换器的强基线模型上，我们的元蒸馏方法能够增强MAML带来的性能提升，并显著优于朴素微调方法。此外，多语言元蒸馏学习甚至能提升对未见语言的泛化能力。