naveenporla/NLP-Pivot-Translation-in-low-resource-environment

Domain:

natural language processing

Record type:

project

Creator:

nav

Host:

# NLP-Pivot-Translation-in-low-resource-environment This Machine Translation project focuses on translating low resource languages that have limited amout of datasets to train the NLP models, here we handle the low resource setting environments by using pivoting techniques where we pivot the source language to a language where we have sufficent datasets to train much accurate models and then translate the pivoted language to target language. In this project we translate Azerbaijani language to English using Turkish as a pivot language so as to improve the quality of translation. The Preprocessed Datasets, Models and Outputs generated in this project can be found in the google repository linked below. drive.google.com

Visit

github.com

Tasks

machine translation

Pivot pre-finetuning for low-resource machine translation

Pivot pre-finetuning for low-resource machine translation

Poster presented at the Deep Learning Indaba 2023 by Stephen Kiilu

Using Interlinear Glosses as Pivot in Low-Resource Multilingual Machine Translation

We demonstrate a new approach to Neural Machine Translation (NMT) for low-resource languages using a

oyinkanchekwas/low-resource-nlp-toolkit

Selective language routing and code-switch audits, evaluated on a reproducible AfriSenti benchmark.

ToluClassics/Low-Resource-NLP-Tutorials

Getting started in NLP for low resource languages # Low-Resource-NLP The goal of this repository i

ellis-nlp/low-resource-MT

Work on machine translation in low resource scenarios and for minority and under-resourced languages

Decolonizing NLP for “Low-resource Languages”

Today African languages are spoken by more than a billion people, yet in the world of machine transl