Impact of Language Pair Complexity on Zero-Shot Cross-Lingual Retrieval in XTR

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 varying the language pair complexity (e.g., typologically similar vs. dissimilar languages) in artificially code-switched data on zero-shot cross-lingual retrieval performance across different language pairs in the XTR benchmark?

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