Comparison of Monolingual and Cross-Lingual Ranking Architectures in Zero-Shot Cross-Lingual Retrieval with Code-Switched 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: How do different monolingual and cross-lingual ranking architectures (e.g., DPR, ANCE, Cross-Encoder) compare in zero-shot cross-lingual retrieval performance when trained on artificially code-switched data, as measured by nDCG@10 on XTREME-R?

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