Impact of Code-Switched Training and Multitask Fine-Tuning on Zero-Shot Cross-Lingual Retrieval Robustness in Multimodal Models

Abstract

Abstract: 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 Question

What is the impact of combining code-switched training with multitask fine-tuning on the robustness of zero-shot cross-lingual retrieval in multimodal models across diverse languages in the MMM benchmark?

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