Zero-Shot Cross-Lingual Retrieval via Artificial Code-Switching Versus Large-Scale Multilingual Pre-training on Low-Resource BEIR

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: How does the performance of zero-shot cross-lingual retrieval models trained on artificially code-switched data compare to multilingual models pre-trained on large-scale multilingual corpora such as mBART or XLM-R in the BEIR benchmark across low-resource language pairs? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.5/10.