Quantifying Domain Adaptation Effects on Cross-Lingual Query-Passage Embedding Alignment in Dense Retrievers

Abstract

Abstract: Effective cross-lingual dense retrieval methods that rely on multilingual pre-trained language models (PLMs) need to be trained to encompass both the relevance matching task and the cross-language alignment task. However, cross-lingual data for training is often scarcely available. In this paper, rather than using more cross-lingual data for training, we propose to use cross-lingual query generation to augment passage representations with queries in languages other than the original passage language. These augmented representations are used at inference time so that the representation can enco

Research Question

How do domain adaptation methods influence the alignment between query and passage embeddings in dense retrievers, and can this alignment be quantified using metrics like cosine similarity or mutual information across languages in benchmarks like XQuAD?

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