The goal of MMT was to deliver a language independent commercial online translation service based on a new open-source machine translation distributed architecture, now available here. MMT does not require any initial training phase. Once fed with training data MMT is ready to translate. MMT de-facto merges translation memory and machine translation technology into one single product. Quality of translations increases as soon as new training data are added. MMT manages context automatically so that it does not require building domain specific systems. MMT provides 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 supports high performance and linear scalability up to thousands of nodes. The same software works 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 created a data collection infrastructure that accelerates the process of filling the data gap between large IT companies and the MT industry. MMT leverages the data crawled on the web by Common Crawl, TAUS, Translated’s MyMemory and Matecat data and facilities to set up a processing pipeline able to create clean parallel and monolingual data to develop machine translation systems.
Funding: Horizon 2020 – IA
1 January 2015 to 31 December 2017 - PROJECT CLOSED