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Robustness of Zero-Shot Cross-Lingual Retrieval Models Against Domain Shift in Low-Resource Languages via Artificial

Domain:

natural language processing
Creator:
Ass
Publisher:
Zenodo
Host:avatar
Transferring information retrieval (IR) models from a high-resource language (typically English) to other languages in a zero-shot fashion has become a widely adopted approach. In this work, we show that the effectiveness of zero-shot rankers diminishes when queries and documents are present in different languages. Motivated by this, we propose to train ranking models on artificially code-switched data instead, which we generate by utilizing bilingual lexicons. To this end, we experiment with lexicons induced from (1) cross-lingual word embeddings and (2) parallel Wikipedia page titles. We use Research goal: How does training on artificially code-switched data affect the robustness of zero-shot cross-lingual retrieval models against domain shift in low-resource language benchmarks? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.5/10.

Visit

doi.orgzenodo.org

Tasks

code switchinginformation retrieval

Tags

trainingartificiallycode-switcheddataaffectrobustnesszero-shotcross-lingual

Licenses

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

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