Synthetic Code-Switched Training Data for Zero-Shot Cross-Lingual Retrieval on BUCC-2018

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 does the integration of synthetic code-switched training data impact the zero-shot cross-lingual retrieval performance (mAP) of language models on Bucc-2018 when evaluated against natural bilingual lexicon variations?

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