Impact of Artificially Code-Switched Training Data on Zero-Shot Cross-Lingual Retrieval Performance in XNLI

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 does the use of artificially code-switched training data impact the zero-shot cross-lingual retrieval performance on the XNLI dataset compared to traditional multilingual fine-tuning methods, as measured by mean average precision (MAP) across low-resource language pairs?

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