# Ensuring the Inclusive Use of Natural Language Processing in the Global Response to COVID-19

arXiv: https://arxiv.org/abs/2108.10791 · HTML: https://mlsanity.com/paper/2108.10791/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 11 Aug 2021.

## Builds on (most cited first, 14 of 14)

- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation](https://mlsanity.com/paper/1611.04558/) (2016)
- [Data Augmentation for Low-Resource Neural Machine Translation](https://mlsanity.com/paper/1705.00440/) (2017)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Low-resource Languages: A Review of Past Work and Future Challenges](https://mlsanity.com/paper/2006.07264/) (2020)
- [r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection](https://mlsanity.com/paper/1911.03854/) (2019)
- [Masakhane -- Machine Translation For Africa](https://mlsanity.com/paper/2003.11529/) (2020)
- [Using Synthetic Audio to Improve The Recognition of Out-Of-Vocabulary Words in End-To-End ASR Systems](https://mlsanity.com/paper/2011.11564/) (2020)
- [BanFakeNews: A Dataset for Detecting Fake News in Bangla](https://mlsanity.com/paper/2004.08789/) (2020)
- [Fame for sale: efficient detection of fake Twitter followers](https://mlsanity.com/paper/1509.04098/) (2015)
- [Mapping the Landscape of Artificial Intelligence Applications against COVID-19](https://mlsanity.com/paper/2003.11336/) (2020)
- [Evaluating Language Model Finetuning Techniques for Low-resource Languages](https://mlsanity.com/paper/1907.00409/) (2019)
- [Automated Speech Generation from UN General Assembly Statements: Mapping Risks in AI Generated Texts](https://mlsanity.com/paper/1906.01946/) (2019)
- [Cross-lingual Transfer Learning for COVID-19 Outbreak Alignment](https://mlsanity.com/paper/2006.03202/) (2020)

## Abstract (arXiv)

Natural language processing (NLP) plays a significant role in tools for the COVID-19 pandemic response, from detecting misinformation on social media to helping to provide accurate clinical information or summarizing scientific research. However, the approaches developed thus far have not benefited all populations, regions or languages equally. We discuss ways in which current and future NLP approaches can be made more inclusive by covering low-resource languages, including alternative modalities, leveraging out-of-the-box tools and forming meaningful partnerships. We suggest several future directions for researchers interested in maximizing the positive societal impacts of NLP.