Zero-Shot Cross-Lingual Rankers Trained on Code-Switched Versus Monolingual Data Under Lexical Mismatch

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 robustness of zero-shot cross-lingual rankers trained on code-switched data compare to those trained on monolingual data when evaluated on noisy bilingual datasets with varying degrees of lexical mismatch?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.2/10.