# Findings of the LoResMT 2021 Shared Task on COVID and Sign Language for Low-resource Languages

arXiv: https://arxiv.org/abs/2108.06598 · HTML: https://mlsanity.com/paper/2108.06598/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 13 references that are in the map.
- First posted on arXiv 14 Aug 2021.

## Builds on (most cited first, 3 of 3)

- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [TICO-19: the Translation Initiative for Covid-19](https://mlsanity.com/paper/2007.01788/) (2020)

## Built on by (most cited first, 10 of 10)

- [adaptMLLM: Fine-Tuning Multilingual Language Models on Low-Resource Languages with Integrated LLM Playgrounds](https://mlsanity.com/paper/2403.02370/) (2024)
- [Machine Translation in the Covid domain: an English-Irish case study for LoResMT 2021](https://mlsanity.com/paper/2403.01196/) (2024)
- [Attentive fine-tuning of Transformers for Translation of low-resourced languages @LoResMT 2021](https://mlsanity.com/paper/2108.08556/) (2021)
- [Human Evaluation of English--Irish Transformer-Based NMT](https://mlsanity.com/paper/2403.02366/) (2024)
- [gaHealth: An English-Irish Bilingual Corpus of Health Data](https://mlsanity.com/paper/2403.03575/) (2024)
- [adaptNMT: an open-source, language-agnostic development environment for Neural Machine Translation](https://mlsanity.com/paper/2403.02367/) (2024)
- [Leveraging LLMs for MT in Crisis Scenarios: a blueprint for low-resource languages](https://mlsanity.com/paper/2410.23890/) (2024)
- [SemiAdapt and SemiLoRA: Efficient Domain Adaptation for Transformer-based Low-Resource Language Translation with a Case Study on Irish](https://mlsanity.com/paper/2510.18725/) (2025)
- [Benchmarking Azerbaijani Neural Machine Translation](https://mlsanity.com/paper/2207.14473/) (2022)
- [BhashaSetu: A Data-Centric Approach to Low-Resource Machine Translation](https://mlsanity.com/paper/2605.27050/) (2026)

## Abstract (arXiv)

We present the findings of the LoResMT 2021 shared task which focuses on machine translation (MT) of COVID-19 data for both low-resource spoken and sign languages. The organization of this task was conducted as part of the fourth workshop on technologies for machine translation of low resource languages (LoResMT). Parallel corpora is presented and publicly available which includes the following directions: EnglishleftrightarrowIrish, EnglishleftrightarrowMarathi, and Taiwanese Sign languageleftrightarrowTraditional Chinese. Training data consists of 8112, 20933 and 128608 segments, respectively. There are additional monolingual data sets for Marathi and English that consist of 21901 segments. The results presented here are based on entries from a total of eight teams. Three teams submitted systems for EnglishleftrightarrowIrish while five teams submitted systems for EnglishleftrightarrowMarathi. Unfortunately, there were no systems submissions for the Taiwanese Sign languageleftrightarrowTraditional Chinese task. Maximum system performance was computed using BLEU and follow as 36.0 for English--Irish, 34.6 for Irish--English, 24.2 for English--Marathi, and 31.3 for Marathi--English.