Depth-Varying Code-Switching in Synthetic Data for Zero-Shot Cross-Lingual Retrieval Performance

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: What is the impact of varying the depth of code-switching (e.g., word-level vs. sentence-level) in synthetic training data on zero-shot cross-lingual retrieval performance in multimodal models, as measured by MRR and NDCG on BEIR and Flickr30k-X?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10.