Impact of Artificial Code-Switching on Cross-Lingual Retrieval Robustness for Low-Resource Languages in MIRACL

Abstract

Abstract: Transferring information retrieval (IR) models from a high-resource language (typically English) to other languages in a zero-shot fashion has become a widely adopted approach. In this work, we show that the effectiveness of zero-shot rankers diminishes when queries and documents are present in different languages. Motivated by this, we propose to train ranking models on artificially code-switched data instead, which we generate by utilizing bilingual lexicons. To this end, we experiment with lexicons induced from (1) cross-lingual word embeddings and (2) parallel Wikipedia page titles. We use

Research Question

To what extent does training on artificially code-switched data improve cross-lingual retrieval robustness for low-resource languages compared to standard translate-train approaches on MIRACL?

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