  State-of-the-art neural retrievers predominantly focus on high-resource
languages like English, which impedes their adoption in retrieval scenarios
involving other languages. Current approaches circumvent the lack of
high-quality labeled data in non-English languages by leveraging multilingual
pretrained language models capable of cross-lingual transfer. However, these
models require substantial task-specific fine-tuning across multiple languages,
often perform poorly in languages with minimal representation in the
pretraining corpus, and struggle to incorporate new languages after the
pretraining phase. In this work, we present a novel modular dense retrieval
model that learns from the rich data of a single high-resource language and
effectively zero-shot transfers to a wide array of languages, thereby
eliminating the need for language-specific labeled data. Our model, ColBERT-XM,
demonstrates competitive performance against existing state-of-the-art
multilingual retrievers trained on more extensive datasets in various
languages. Further analysis reveals that our modular approach is highly
data-efficient, effectively adapts to out-of-distribution data, and
significantly reduces energy consumption and carbon emissions. By demonstrating
its proficiency in zero-shot scenarios, ColBERT-XM marks a shift towards more
sustainable and inclusive retrieval systems, enabling effective information
accessibility in numerous languages. We publicly release our code and models
for the community.
