QT21: Quality Translation 21 Project
Quality Translation 21 is a machine translation project which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 645452.
A European Digital Single Market free of barriers, including language barriers, is a stated EU objective to be achieved by 2020. The findings of the META-NET Language White Papers show that currently only 3 of the EU-27 languages enjoy moderate to good support by our machine translation technologies, with either weak (at best fragmentary) or no support for the vast majority of the EU-27 languages. This lack is a key obstacle impeding the free flow of people, information and trade in the European Digital Single Market.
Many of the languages not supported by our current technologies show common traits: they are morphologically complex, with free and diverse word order. Often there are not enough training resources and/or processing tools. Together this results in drastic drops in translation quality. The combined challenges of linguistic phenomena and resource scenarios have created a large and under-explored grey area in the language technology map of European languages. Combining support from key stakeholders, QT21 addresses this grey area developing
- substantially improved statistical and machine-learning based translation models for challenging languages and resource scenarios,
- improved evaluation and continuous learning from mistakes, guided by a systematic analysis of quality barriers, informed by human translators,
- all with a strong focus on scalability, to ensure that learning and decoding with these models is efficient and that reliance on data (annotated or not) is minimised.
To continuously measure progress, and to provide a platform for sharing and collaboration (QT21 internally and beyond), the project revolves around a series of Shared Tasks, for maximum impact co-organised with the annual workshops on machine translation (WMT).
Duration: 1st February 2015 – 31st January 2018
View Past and Upcoming GALA QT21 Webinars:
- Harmonizing Quality—TAUS DQF and MQM Become a Single Quality Framework (July 2015)
- Next Generation Services for Custom Machine Translation (April 2016)
- How does Modern Machine Translation Work? A Story in Pictures, Not Math (Jan and July 2016)
- Automatic Evaluation of MT: Moving Away from Word Matching Metrics (Sept 2016)
- Automatically Correct MT Errors from Human Post-edited Data (Dec 2016)
- Improving Your Quality Management Processes Using the DQF-MQM Harmonized Error Typology (March 2017)
- Performing Error Review using the DQF-MQM Harmonized Error Typology (March 2017)
- Comparing Errors of Neural MT with Errors of “Traditional” Phrase-based and Rule-based MT (May 2017)
- Challenges of Domain Adaptation for MT Systems (Sept 2017)
- Machine Translation: How LSPs Benefit from Three Years of European Research in QT21 (March 2018)