Domain-specific bilingual lexicons and their impact on artificial code-switching for zero-shot cross-lingual retrieval

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

How do variations in the bilingual lexicon's domain specificity (e.g., biomedical vs. general) impact the quality of artificially code-switched data and subsequent zero-shot cross-lingual retrieval performance on TREC COVID?

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