Query Augmentation for Cross-Lingual Dense Retrieval in Low-Resource Languages
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 goal: Does query augmentation for cross-lingual dense retrieval maintain accuracy on low-resource languages when evaluated on the XQuAD benchmark compared to mT5-based passage generation? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.0/10.
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