Abstract

Multilingual semantic retrieval involves retrieving semantically relevant content to a query irrespective of the language. Compared to monolingual and bilingual semantic retrieval, multilingual semantic retrieval requires a stronger alignment approach to pull the contents to be retrieved close to the representation of their corresponding queries, no matter their language combinations. Traditionally, this is achieved through more supervision in the form of multilingual parallel resources, which are expensive to obtain, especially for low-resource languages. In this work, on top of an optimization-based Model-Agnostic Meta-Learner (MAML), we propose a data-efficient meta-distillation approach: MAML-Align,1 specifically for low-resource multilingual semantic retrieval. Our approach simulates a gradual feedback loop from monolingual to bilingual and from bilingual to multilingual semantic retrieval. We systematically …

Metadata

publication

Proceedings of the 47th International ACM SIGIR Conference on Research and …, 2024

year

2024

publication date

2024/7/10

authors

Meryem M'hamdi, Jonathan May, Franck Dernoncourt, Trung Bui, Seunghyun Yoon

link

https://dl.acm.org/doi/abs/10.1145/3626772.3657812

resource_link

https://dl.acm.org/doi/pdf/10.1145/3626772.3657812

book

Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval

pages

597-607