Extremely low-resource machine translation for closely related languages

05/27/2021
by   Maali Tars, et al.
0

An effective method to improve extremely low-resource neural machine translation is multilingual training, which can be improved by leveraging monolingual data to create synthetic bilingual corpora using the back-translation method. This work focuses on closely related languages from the Uralic language family: from Estonian and Finnish geographical regions. We find that multilingual learning and synthetic corpora increase the translation quality in every language pair for which we have data. We show that transfer learning and fine-tuning are very effective for doing low-resource machine translation and achieve the best results. We collected new parallel data for Võro, North and South Saami and present first results of neural machine translation for these languages.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/01/2019

A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation

Neural machine translation has become the state-of-the-art for language ...
research
11/10/2020

Neural Machine Translation for Extremely Low-Resource African Languages: A Case Study on Bambara

Low-resource languages present unique challenges to (neural) machine tra...
research
04/08/2020

Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation

There are several approaches for improving neural machine translation fo...
research
09/27/2022

Improving Multilingual Neural Machine Translation System for Indic Languages

Machine Translation System (MTS) serves as an effective tool for communi...
research
06/09/2022

Dict-NMT: Bilingual Dictionary based NMT for Extremely Low Resource Languages

Neural Machine Translation (NMT) models have been effective on large bil...
research
08/04/2020

A Survey of Orthographic Information in Machine Translation

Machine translation is one of the applications of natural language proce...
research
09/28/2022

An Automatic Evaluation of the WMT22 General Machine Translation Task

This report presents an automatic evaluation of the general machine tran...

Please sign up or login with your details

Forgot password? Click here to reset