Mitigating Cross-Lingual Ranker Degradation via Artificially Code-Switched Training Data in the MKQA Dataset

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 artificially code-switched training data mitigate the performance degradation of cross-lingual rankers when query and document languages differ in the MKQA dataset?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 7.7/10.