Scalability of Artificially Code-Switched Data for Low-Resource Language Rankers in MTOP

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: Does the scalability of artificially code-switched data for training rankers hold when applied to low-resource languages in the MTOP dataset, and how does it compare to zero-shot cross-lingual transfer from high-resource languages in terms of retrieval performance metrics like nDCG and MAP?

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