Improving Zero-Shot Cross-Lingual Ranker Robustness via Artificial Code-Switching Training

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: Does training on artificially code-switched data improve the robustness of zero-shot cross-lingual rankers against language mismatch errors compared to standard monolingual transfer methods on MTOP?

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