Konstantin Savenkov


2022

pdf bib
Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)
Janice Campbell | Stephen Larocca | Jay Marciano | Konstantin Savenkov | Alex Yanishevsky
Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)

bib
The State of the Machine Translation 2022
Konstantin Savenkov | Michel Lopez
Proceedings of the 15th Biennial Conference of the Association for Machine Translation in the Americas (Volume 2: Users and Providers Track and Government Track)

In this talk, we cover the 2022 annual report on State of the Machine Translation, prepared together by Intento and e2f. The report analyses the performance of 20+ commercial MT engines across 9 industries (General, Colloquial, Education, Entertainment, Financial, Healthcare, Hospitality, IT, and Legal) and 10+ key language pairs. For the first time, this report is run using a unique dataset covering all language/domain combinations above, prepared by e2f. The presentation would focus on the process of data selection and preparation, the report methodology, principal scores to rely on when studying MT outcomes (COMET, BERTScore, PRISM, TER, and hLEPOR), and the main report outcomes (best performing MT engines for every language / domain combination). It includes a thorough comparison of the scores. It also covers language support, prices, and other features of the MT engines.

2021

pdf bib
Proceedings of Machine Translation Summit XVIII: Users and Providers Track
Janice Campbell | Ben Huyck | Stephen Larocca | Jay Marciano | Konstantin Savenkov | Alex Yanishevsky
Proceedings of Machine Translation Summit XVIII: Users and Providers Track

bib
Preserving high MT quality for content with inline tags
Konstantin Savenkov | Grigory Sapunov | Pavel Stepachev
Proceedings of Machine Translation Summit XVIII: Users and Providers Track

Attendees will learn about how we use machine translation to provide targeted, high MT quality for content with inline tags. We offer a new and innovative approach to inserting tags into the translated text in a way that reliably preserves their quality. This process can achieve better MT quality and lower costs, as it is MT-independent, and can be used for all languages, MT engines, and use cases.

2020

bib
Building Multi-Purpose MT Portfolio
Konstantin Savenkov
Proceedings of the 14th Conference of the Association for Machine Translation in the Americas (Volume 2: User Track)