Quality Prediction
Abstract
A growing share of machine translations are approved - untouched - by human translators in post-editing workflows. But they still cost time and money. Now companies are getting human post-editing quality faster and cheaper, by automatically approving the good machine translations - at human accuracy. The approach has evolved, from research papers on machine translation quality estimation, to adoption inside companies like Amazon, Facebook, Microsoft and VMWare, to self-serve cloud APIs like ModelFront. We’ll walk through the motivations, use cases, prerequisites, adopters, providers, integration and ROI.- Anthology ID:
- 2022.amta-upg.12
- Volume:
- Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)
- Month:
- September
- Year:
- 2022
- Address:
- Orlando, USA
- Editors:
- Janice Campbell, Stephen Larocca, Jay Marciano, Konstantin Savenkov, Alex Yanishevsky
- Venue:
- AMTA
- SIG:
- Publisher:
- Association for Machine Translation in the Americas
- Note:
- Pages:
- 159–180
- Language:
- URL:
- https://preview.aclanthology.org/icon-24-ingestion/2022.amta-upg.12/
- DOI:
- Cite (ACL):
- Adam Bittlingmayer, Boris Zubarev, and Artur Aleksanyan. 2022. Quality Prediction. In Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track), pages 159–180, Orlando, USA. Association for Machine Translation in the Americas.
- Cite (Informal):
- Quality Prediction (Bittlingmayer et al., AMTA 2022)