Fine-tuning effects on zero-shot cross-lingual retrieval with domain-specific code-switched data

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: To what extent does fine-tuning zero-shot cross-lingual retrieval models on domain-specific code-switched data (e.g., legal or medical corpora) improve retrieval accuracy compared to general-domain code-switched training, as measured by MRR or NDCG on domain-specific benchmarks?

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