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

arXiv: https://arxiv.org/abs/2306.09830 · HTML: https://mlsanity.com/paper/2306.09830/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://mlsanity.com/paper/1904.01038/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [The Tatoeba Translation Challenge -- Realistic Data Sets for Low Resource and Multilingual MT](https://mlsanity.com/paper/2010.06354/) (2020)
- [Survey of Low-Resource Machine Translation](https://mlsanity.com/paper/2109.00486/) (2021)

## Built on by (most cited first, 4 of 4)

- [NLP Progress in Indigenous Latin American Languages](https://mlsanity.com/paper/2404.05365/) (2024)
- [Limitations of Religious Data and the Importance of the Target Domain: Towards Machine Translation for Guinea-Bissau Creole](https://mlsanity.com/paper/2504.02674/) (2025)
- [Improving Indigenous Language Machine Translation with Synthetic Data and Language-Specific Preprocessing](https://mlsanity.com/paper/2601.03135/) (2026)
- [Retrieval-Augmented Long-Context Translation for Cultural Image Captioning: Gators submission for AmericasNLP 2026 shared task](https://mlsanity.com/paper/2605.20626/) (2026)

## Abstract (arXiv)

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.