# Milimili. Collecting Parallel Data via Crowdsourcing

arXiv: https://arxiv.org/abs/2307.12282 · HTML: https://mlsanity.com/paper/2307.12282/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 6 references that are in the map.
- First posted on arXiv 23 Jul 2023.

## Builds on (most cited first, 4 of 4)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [CrowdSpeech and VoxDIY: Benchmark Datasets for Crowdsourced Audio Transcription](https://mlsanity.com/paper/2107.01091/) (2021)

## Abstract (arXiv)

We present a methodology for gathering a parallel corpus through crowdsourcing, which is more cost-effective than hiring professional translators, albeit at the expense of quality. Additionally, we have made available experimental parallel data collected for Chechen-Russian and Fula-English language pairs.