Authors: Antoine Louis, Vageesh Saxena, Gijs van Dijck, Gerasimos Spanakis

Published on: February 23, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2402.15059

Summary

• What is new: A modular dense retrieval model, ColBERT-XM, that learns from one high-resource language and effectively transfers knowledge to multiple other languages without needing language-specific labeled data.
• Why this is important: State-of-the-art neural retrievers have limited efficacy in languages other than English due to the lack of high-quality labeled data and the challenges in cross-lingual transfer.
• What the research proposes: ColBERT-XM, a modular dense retrieval model that leverages data from a single high-resource language to achieve competitive performance in various other languages in a zero-shot manner.
• Results: ColBERT-XM performs competitively against state-of-the-art multilingual retrievers across numerous languages without needing extensive datasets for each one, demonstrating data efficiency, adaptability to out-of-distribution data, and reductions in energy consumption and carbon emissions.

Technical Details

Technological frameworks used: nan

Models used: ColBERT-XM

Data used: Data from a single high-resource language

Potential Impact

Language technology services, search engines, and content retrieval platforms, especially those operating in multilingual or low-resource language environments.

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