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.

What is the impact of synthetic data augmentation on low-resource machine translation quality

Domaine:

natural language processing

Type de record:

paper
Créateur:
SOV
Éditeur:
Zenodo
Hôte:avatar
One important issue that affects the performance of neural machine translation is the scale of available parallel data. For low-resource languages, the amount of parallel data is not sufficient, which results in poor translation quality. In this paper, we propose a diversity data augmentation method that does not use extra monolingual data. We expand the training data by generating diversity pseudo parallel data on the source and target sides. To generate diversity data, the restricted sampling strategy is employed at the decoding steps. Finally, we filter and merge origin data and synthetic p Research goal: What is the impact of synthetic data augmentation on low-resource machine translation quality? Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.5/10. This report was generated autonomously by SOVEREIGN Research Kernel, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 7.5/10.

Visit

doi.orgzenodo.org

Tasks

machine translation

Tags

impactsyntheticdataaugmentationlow-resourcemachinetranslationquality

Licenses

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

Similaires

Data Augmentation for Low-Resource Neural Machine TranslationA Diverse Data Augmentation Strategy for Low-Resource Neural Machine TranslationData Augmentation for Low Resource Neural Machine Translation for Sotho-Tswana LanguagesImproving low-resource neural machine translation by semantic distance augmentationTextual Augmentation Techniques Applied to Low Resource Machine Translation: Case of SwahiliTranslatePsy-AfriSLM: High-Quality Data Scaling For Low-Resource Machine Translation

Data Augmentation for Low-Resource Neural Machine Translation

The quality of a Neural Machine Translation system depends substantially on the availability of siza

A Diverse Data Augmentation Strategy for Low-Resource Neural Machine Translation

One important issue that affects the performance of neural machine translation is the scale of avail

Data Augmentation for Low Resource Neural Machine Translation for Sotho-Tswana Languages

Neural Machine Translation (NMT) models have achieved remarkable performance on translating

Improving low-resource neural machine translation by semantic distance augmentation

Neural machine translation (NMT) has witnessed substantial advancements, leveraging its learning cap

Textual Augmentation Techniques Applied to Low Resource Machine Translation: Case of Swahili

In this work we investigate the impact of applying textual data augmentation tasks to low resource machine translation. There has been recent interest in investigating approaches for training systems for languages with limited resources and one popular approach is

TranslatePsy-AfriSLM: High-Quality Data Scaling For Low-Resource Machine Translation

The rapid progress in Artificial Intelligence has largely bypassed African languages, creating a dig