# Learnings from Technological Interventions in a Low Resource Language: Enhancing Information Access in Gondi

arXiv: https://arxiv.org/abs/2211.16172 · HTML: https://mlsanity.com/paper/2211.16172/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 65 references that are in the map.
- First posted on arXiv 29 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets](https://mlsanity.com/paper/2103.12028/) (2021)
- [Language ID in the Wild: Unexpected Challenges on the Path to a Thousand-Language Web Text Corpus](https://mlsanity.com/paper/2010.14571/) (2020)
- [On the Impact of Various Types of Noise on Neural Machine Translation](https://mlsanity.com/paper/1805.12282/) (2018)
- [Beyond Fair Pay: Ethical Implications of NLP Crowdsourcing](https://mlsanity.com/paper/2104.10097/) (2021)
- [Expanding Pretrained Models to Thousands More Languages via Lexicon-based Adaptation](https://mlsanity.com/paper/2203.09435/) (2022)
- [OCR Post Correction for Endangered Language Texts](https://mlsanity.com/paper/2011.05402/) (2020)
- [What Ingredients Make for an Effective Crowdsourcing Protocol for Difficult NLU Data Collection Tasks?](https://mlsanity.com/paper/2106.00794/) (2021)
- [Unsung Challenges of Building and Deploying Language Technologies for Low Resource Language Communities](https://mlsanity.com/paper/1912.03457/) (2019)
- [Unmet Needs and Opportunities for Mobile Translation AI](https://mlsanity.com/paper/2002.12387/) (2020)
- [Toward Annotator Group Bias in Crowdsourcing](https://mlsanity.com/paper/2110.08038/) (2021)
- [Quantifying and Avoiding Unfair Qualification Labour in Crowdsourcing](https://mlsanity.com/paper/2105.12762/) (2021)
- [User-friendly automatic transcription of low-resource languages: Plugging ESPnet into Elpis](https://mlsanity.com/paper/2101.03027/) (2020)
- [Learnings from Technological Interventions in a Low Resource Language: A Case-Study on Gondi](https://mlsanity.com/paper/2004.10270/) (2020)
- [The Extraordinary Failure of Complement Coercion Crowdsourcing](https://mlsanity.com/paper/2010.05971/) (2020)

## Abstract (arXiv)

The primary obstacle to developing technologies for low-resource languages is the lack of representative, usable data. In this paper, we report the deployment of technology-driven data collection methods for creating a corpus of more than 60,000 translations from Hindi to Gondi, a low-resource vulnerable language spoken by around 2.3 million tribal people in south and central India. During this process, we help expand information access in Gondi across 2 different dimensions (a) The creation of linguistic resources that can be used by the community, such as a dictionary, children's stories, Gondi translations from multiple sources and an Interactive Voice Response (IVR) based mass awareness platform; (b) Enabling its use in the digital domain by developing a Hindi-Gondi machine translation model, which is compressed by nearly 4 times to enable it's edge deployment on low-resource edge devices and in areas of little to no internet connectivity. We also present preliminary evaluations of utilizing the developed machine translation model to provide assistance to volunteers who are involved in collecting more data for the target language. Through these interventions, we not only created a refined and evaluated corpus of 26,240 Hindi-Gondi translations that was used for building the translation model but also engaged nearly 850 community members who can help take Gondi onto the internet.