Scaling Artificially Code-Switched Training Data for Zero-Shot Multilingual Dense Retrieval in Low-Resource Languages

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 scaling the volume of artificially code-switched training data on the zero-shot nDCG@10 performance of multilingual dense retrievers across low-resource languages?

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