Multilingual Information Retrieval with a Monolingual Knowledge Base
This addresses cross-language knowledge sharing, particularly for transferring knowledge from high-resource to low-resource languages, but is incremental as it builds on existing multilingual embedding methods.
The paper tackles the problem of multilingual information retrieval using a monolingual knowledge base by fine-tuning embedding models with a weighted sampling strategy for contrastive learning, achieving performance gains of up to 31.03% in MRR and 33.98% in Recall@3.
Multilingual information retrieval has emerged as powerful tools for expanding knowledge sharing across languages. On the other hand, resources on high quality knowledge base are often scarce and in limited languages, therefore an effective embedding model to transform sentences from different languages into a feature vector space same as the knowledge base language becomes the key ingredient for cross language knowledge sharing, especially to transfer knowledge available in high-resource languages to low-resource ones. In this paper we propose a novel strategy to fine-tune multilingual embedding models with weighted sampling for contrastive learning, enabling multilingual information retrieval with a monolingual knowledge base. We demonstrate that the weighted sampling strategy produces performance gains compared to standard ones by up to 31.03\% in MRR and up to 33.98\% in Recall@3. Additionally, our proposed methodology is language agnostic and applicable for both multilingual and code switching use cases.