Sheffield's Submission to the AmericasNLP Shared Task on Machine Translation into Indigenous Languages

In this paper we describe the University of Sheffield's submission to the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages which comprises the translation from Spanish to eleven indigenous languages. Our approach consists of extending, training, and ensembling diffe...

Full description

Saved in:
Bibliographic Details
Main Authors Gow-Smith, Edward, Villegas, Danae Sánchez
Format Journal Article
LanguageEnglish
Published 16.06.2023
Subjects
Computer Science - Computation and Language
Online AccessGet full text

Cover

More Information
Summary:In this paper we describe the University of Sheffield's submission to the AmericasNLP 2023 Shared Task on Machine Translation into Indigenous Languages which comprises the translation from Spanish to eleven indigenous languages. Our approach consists of extending, training, and ensembling different variations of NLLB-200. We use data provided by the organizers and data from various other sources such as constitutions, handbooks, news articles, and backtranslations generated from monolingual data. On the dev set, our best submission outperforms the baseline by 11% average chrF across all languages, with substantial improvements particularly for Aymara, Guarani and Quechua. On the test set, we achieve the highest average chrF of all the submissions, we rank first in four of the eleven languages, and at least one of our submissions ranks in the top 3 for all languages.
DOI:10.48550/arxiv.2306.09830