Fine-tuning Retrieval Models on Mixed-Domain Code-Switched Data for Zero-Shot Language Generalization

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 fine-tuning retrieval models on mixed-domain (e.g., news + scientific) artificially code-switched data affect their zero-shot generalization to unseen languages, measured by retrieval accuracy on the LASER benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.