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Query Augmentation for Cross-Lingual Dense Retrieval in Low-Resource Languages

Domain:

natural language processing

Record type:

paper
Creator:
Ass
Publisher:
Zenodo
Host:avatar
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. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.0/10.

Visit

doi.orgzenodo.org

Tasks

information retrieval

Tags

queryaugmentationcross-lingualdenseretrievalmaintainaccuracylow-resource

Licenses

Creative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

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