MMT | MMT will deliver a language independent commercial online translation service based on a new open-source machine translation distributed architecture

Summary
The goal of MMT is to deliver a language independent commercial online translation service based on a new open-source machine translation distributed architecture.

MMT does not require any initial training phase. Once fed with training data MMT will be ready to translate. MMT de-facto will merge translation memory and machine translation technology into one single product. Quality of translations will increase as soon as new training data are added.

MMT manages context automatically so that it will not require building domain specific systems. MMT will provide best translation quality for any topic/domain by storing training segments together with context linking information.

MMT enables scalability of data and users so that no more expensive ad-hoc hardware installations are needed. The MMT architecture will support high performance and linear scalability up to thousands of nodes. The same software will work to set-up a personal translation system or to create a web-based service on a cluster of commodity nodes able to handle terabytes of data and millions of users.

MMT will create a data collection infrastructure that accelerates the process of filling the data gap between large IT companies and the MT industry. MMT will leverage the data crawled on the web by Common Crawl, TAUS, Translated’s MyMemory and Matecat data and facilities to set up a processing pipeline that will create unprecedented amounts of clean parallel and monolingual data to develop machine translation systems.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/645487
Start date: 01-01-2015
End date: 31-12-2017
Total budget - Public funding: 3 695 200,00 Euro - 2 994 700,00 Euro
Cordis data

Original description

The goal of MMT is to deliver a language independent commercial online translation service based on a new open-source machine translation distributed architecture.

MMT does not require any initial training phase. Once fed with training data MMT will be ready to translate. MMT de-facto will merge translation memory and machine translation technology into one single product. Quality of translations will increase as soon as new training data are added.

MMT manages context automatically so that it will not require building domain specific systems. MMT will provide best translation quality for any topic/domain by storing training segments together with context linking information.

MMT enables scalability of data and users so that no more expensive ad-hoc hardware installations are needed. The MMT architecture will support high performance and linear scalability up to thousands of nodes. The same software will work to set-up a personal translation system or to create a web-based service on a cluster of commodity nodes able to handle terabytes of data and millions of users.

MMT will create a data collection infrastructure that accelerates the process of filling the data gap between large IT companies and the MT industry. MMT will leverage the data crawled on the web by Common Crawl, TAUS, Translated’s MyMemory and Matecat data and facilities to set up a processing pipeline that will create unprecedented amounts of clean parallel and monolingual data to develop machine translation systems.

Status

CLOSED

Call topic

ICT-17-2014

Update Date

26-10-2022
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Horizon 2020
H2020-EU.2. INDUSTRIAL LEADERSHIP
H2020-EU.2.1. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies
H2020-EU.2.1.1. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies - Information and Communication Technologies (ICT)
H2020-EU.2.1.1.4. Content technologies and information management: ICT for digital content, cultural and creative industries
H2020-ICT-2014-1
ICT-17-2014 Cracking the language barrier