Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

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

Domaine:

natural language processing
Créateur:
Ass
Éditeur:
Zenodo
Hôte: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

Similaires

Domain Adaptation Effects on Zero-Shot Cross-Lingual Retrieval Robustness in Low-Resource LanguagesRobustness of Zero-Shot Cross-Lingual Retrieval Models via Code-Switched Pre-Training in Low-Resource LanguagesRobustness of Zero-Shot Cross-Lingual Retrieval Models Against Adversarial Noise in Low-Resource Language PairsZero-Shot Cross-Lingual Retrieval Degradation on Low-Resource Languages via Artificial Code-SwitchingRobustness of Cross-Lingual Retrieval Models via Optimal Transport Distillation Under Domain Shifts in Low-Resource LanguagesRobustness of Cross-Lingual NER Models via Domain Adaptation in Low-Resource Languages

Domain Adaptation Effects on Zero-Shot Cross-Lingual Retrieval Robustness in Low-Resource Languages

Transferring information retrieval (IR) models from a high-resource language (typically English) to

Robustness of Zero-Shot Cross-Lingual Retrieval Models via Code-Switched Pre-Training in Low-Resource Languages

Transferring information retrieval (IR) models from a high-resource language (typically English) to

Robustness of Zero-Shot Cross-Lingual Retrieval Models Against Adversarial Noise in Low-Resource Language Pairs

Transferring information retrieval (IR) models from a high-resource language (typically English) to

Zero-Shot Cross-Lingual Retrieval Degradation on Low-Resource Languages via Artificial Code-Switching

Transferring information retrieval (IR) models from a high-resource language (typically English) to

Robustness of Cross-Lingual Retrieval Models via Optimal Transport Distillation Under Domain Shifts in Low-Resource Languages

Benefiting from transformer-based pre-trained language models, neural ranking models have made signi

Robustness of Cross-Lingual NER Models via Domain Adaptation in Low-Resource Languages

Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to ident