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Low Resourced Multilingual Neural Machine Translation for Ometo-English

Domain:

natural language processing

Record type:

datasetpaper
Creator:
AssWol
Publisher:
Und
Host:avatar
In this paper, we present a new approach to overcome the problem of language resources that share significant amount of linguistic resource in the Ometo language family. The dataset is collected from religious domain consisting of four Ometo (Wolaita, Gamo, Gofa, and Dawuro) languages paired with English sentence. Among the experiments, the Wolaita, Dawuro and Gamo paired with English sentence combination for training and Gofa for testing gives highest BLEU score 4.5 than the other combinations. The BLEU score shows a promising result despite the language differences, the morphological richness, and complexity of the Ometo languages which has high impact on the performance of the Ometo-English machine translation.

Visit

doi.orgunderline.io

Tasks

machine translation

Languages

DawroGamoGofaWolaytta

Tags

Natural Language ProcessingMachine translation

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