Model Merging of Specialized Monolingual Retrievers versus Jointly Trained Multilingual Models for Domain-Specific Cross-Lingual

Abstract

Abstract: In this work we present a systematic empirical study focused on the suitability of the state-of-the-art multilingual encoders for cross-lingual document and sentence retrieval tasks across a number of diverse language pairs. We first treat these models as multilingual text encoders and benchmark their performance in unsupervised ad-hoc sentence- and document-level CLIR. In contrast to supervised language understanding, our results indicate that for unsupervised document-level CLIR – a setup with no relevance judgments for IR-specific fine-tuning – pretrained multilingual encoders on average

Research Question

Does model merging of specialized monolingual retrievers outperform jointly trained multilingual models on domain-specific cross-lingual retrieval tasks?

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