Query Generation Augmentation for Robust Dense Retrieval Across Language Families in Low-Resource XQuAD Subsets

Abstract

Abstract: The dual-encoder model is a dense retrieval architecture, consisting of two encoder models, that has surpassed traditional sparse retrieval methods for open-domain retrieval [1]. But, room exists for improvement, particularly when dense retrievers are exposed to unseen passages or queries. Considering out-of-domain queries, i.e., queries originating from domains other than the one the model was trained on, the loss in accuracy may be significant. A main factor for this is the mismatch in the information available to the context encoder and the query encoder during training. Common retireval tr

Research Question

Does the query generation augmentation method enhance robustness against language family divergence in dense retrievers as measured by retrieval accuracy drops on low-resource language subsets of XQuAD?

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