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Milimili. Collecting Parallel Data via Crowdsourcing

arXiv 2307.12282 ↗First posted 23 Jul 2023cs.CLcited by 0

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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.

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.

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References · 4

  1. 2017Attention Is All You Needcited by 60,361
  2. 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
  3. 2016FastText.zip: Compressing text classification modelscited by 335
  4. 2021CrowdSpeech and VoxDIY: Benchmark Datasets for Crowdsourced Audio Transcriptioncited by 9

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