Robustness of Zero-Shot Cross-Lingual Retrieval Models via Code-Switched Pre-Training in Low-Resource Languages

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 goal: To what extent does pre-training on artificially code-switched data improve the robustness of zero-shot cross-lingual retrieval models against domain shifts in low-resource languages, evaluated using nDCG on domain-specific subsets of MIRACL? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.6/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.6/10.