Query-Synergy: Leveraging High-Resource Languages for Improving Retrieval Performance Across Multiple Languages

2026-06-26 · Source: Paper Index on ACL Anthology · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, quick

Summary

Query-Synergy is a training-free method designed to enhance multilingual information retrieval by addressing the uneven representational quality of multilingual embedding models, which typically favor high-resource languages like English. This approach complements source-language queries with additional English queries, integrating similarity scores from both to exploit the superior semantic expressiveness of high-resource language subspaces. Evaluated across five languages—Arabic, Chinese, Greek, Thai, and Turkish—using four distinct multilingual embedding models on two datasets, Query-Synergy consistently outperformed conventional source query retrieval methods. The method achieved superior nDCG scores across various configurations and translation settings, confirming its effectiveness as a simple solution for improving retrieval performance across multiple languages.

Key takeaway

For Machine Learning Engineers developing multilingual retrieval systems, Query-Synergy offers a straightforward, training-free approach to improve performance. If your current system relies solely on source-language queries, consider implementing this method by generating additional English queries and integrating their similarity scores. This can significantly enhance nDCG scores across diverse languages like Arabic, Chinese, Greek, Thai, and Turkish, without requiring complex model retraining or extensive resource investment.

Key insights

Leveraging high-resource language queries improves multilingual retrieval performance by integrating complementary semantic expressiveness.

Principles

• Multilingual embeddings favor high-resource languages.
• Complementary queries enhance retrieval accuracy.
• Integrating scores from multiple query types is effective.

Method

Query-Synergy uses additional English queries alongside source language queries. It then integrates similarity scores from both query types to boost retrieval performance.

In practice

• Implement dual-query systems for multilingual search.
• Utilize English as a bridge language for non-English queries.
• Combine similarity scores from different query representations.

Topics

• Multilingual Information Retrieval
• Query Expansion
• Multilingual Embeddings
• High-Resource Languages
• nDCG Scores
• Cross-lingual Search

Best for: AI Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Paper Index on ACL Anthology.