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.

Data Augmentation for Cross-Lingual NER Exact Match Accuracy on FLORES-200 Code-Mixed Low-Resource Languages

Domaine:

natural language processing

Type de record:

paper
Créateur:
Ass
Éditeur:
Zenodo
Hôte:avatar
Cross-lingual Named Entity Recognition (NER) leverages knowledge transfer between languages to identify and classify named entities, making it particularly useful for low-resource languages. We show that the data-based cross-lingual transfer method is an effective technique for crosslingual NER and can outperform multilingual language models for low-resource languages. This paper introduces two key enhancements to the annotation projection step in cross-lingual NER for low-resource languages. First, we explore refining word alignments using back-translation to improve accuracy. Second, we pres Research goal: What is the impact of data augmentation techniques on the exact match accuracy of cross-lingual NER models when evaluated on the FLORES-200 benchmark for code-mixed low-resource languages? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.1/10. This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 8.1/10.

Visit

doi.org

Tasks

named entity recognitioninformation extraction

Tags

impactdataaugmentationtechniquesexactmatchaccuracycross-lingual

Licenses

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

Similaires

MIRACL Retrieval Accuracy via Combined Monolingual, Cross-Lingual, and Multilingual Data Augmentation for Low-Resource LanguagesScaling Diverse Source Languages for Cross-Lingual NER Accuracy in Low-Resource TargetsMultilingual Intermediate-Task Training for Low-Resource Languages on FLORES-200Cross-Lingual NER for Financial Transaction Data in Low-Resource LanguagesCross-lingual NER Transfer Accuracy and Unlabeled Target-Language Data Volume in Low-Resource LanguagesScaling Training Data and Accuracy Disparity in Low-Resource Cross-Lingual NER

MIRACL Retrieval Accuracy via Combined Monolingual, Cross-Lingual, and Multilingual Data Augmentation for Low-Resource Languages

Information retrieval across different languages is an increasingly important challenge in natural l

Scaling Diverse Source Languages for Cross-Lingual NER Accuracy in Low-Resource Targets

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

Multilingual Intermediate-Task Training for Low-Resource Languages on FLORES-200

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuni

Cross-Lingual NER for Financial Transaction Data in Low-Resource Languages

We propose an efficient modeling framework for cross-lingual named entity recognition in semi-struct

Cross-lingual NER Transfer Accuracy and Unlabeled Target-Language Data Volume in Low-Resource Languages

Multilingual Language Models (MLLMs) exhibit robust cross-lingual transfer capabilities, or the abil

Scaling Training Data and Accuracy Disparity in Low-Resource Cross-Lingual NER

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