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.

Analysing Cross-Lingual Transfer in Low-Resourced African Named Entity Recognition

Domaine:

natural language processing

Type de record:

paper
Créateur:
BeuFok
Hôte:avatar
Transfer learning has led to large gains in performance for nearly all NLP tasks while making downstream models easier and faster to train. This has also been extended to low-resourced languages, with some success. We investigate the properties of cross-lingual transfer learning between ten low-resourced languages, from the perspective of a named entity recognition task. We specifically investigate how much adaptive fine-tuning and the choice of transfer language affect zero-shot transfer performance. We find that models that perform well on a single language often do so at the expense of generalising to others, while models with the best generalisation to other languages suffer in individual language performance. Furthermore, the amount of data overlap between the source and target datasets is a better predictor of transfer performance than either the geographical or genetic distance between the languages. Accepted to IJCNLP-AACL 2023

Visit

arxiv.org

Tasks

information extractionnamed entity recognitiontransfer learning

Tags

Computation and Language

Similaires

Analysing the effects of transfer learning on low-resourced named entity recognition performancePerformance comparison of cross-lingual transfer learning methods for Named Entity Recognition in low-resource African languagesCross-lingual Transfer Performance in Low-resource Named Entity Recognition via Transliterated Scripts and Expanded VocabulariesNamed Entity Recognition in Low-resource Languages using Cross-lingual distributional word representationLow-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random FieldsMeta-Pretraining for Zero-Shot Cross-Lingual Named Entity Recognition in Low-Resource Philippine Languages

Analysing the effects of transfer learning on low-resourced named entity recognition performance

Transfer learning has led to large gains in performance for nearly all NLP tasks while making downstream models easier and faster to train. This has also been extended to low-resourced languages, with some success. We investigate the properties of transfer learning

Performance comparison of cross-lingual transfer learning methods for Named Entity Recognition in low-resource African languages

Cross-lingual transfer learning enables NLP for low-resource languages by leveraging labeled data fr

Cross-lingual Transfer Performance in Low-resource Named Entity Recognition via Transliterated Scripts and Expanded Vocabularies

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

Named Entity Recognition in Low-resource Languages using Cross-lingual distributional word representation

Named Entity Recognition (NER) is a fundamental task in many NLP applications that seek to identify

Low-Resource Named Entity Recognition with Cross-Lingual, Character-Level Neural Conditional Random Fields

Low-resource named entity recognition is still an open problem in NLP. Most state-of-the-art systems

Meta-Pretraining for Zero-Shot Cross-Lingual Named Entity Recognition in Low-Resource Philippine Languages

Named-entity recognition (NER) in low-resource languages is usually tackled by finetuning very large