DCU Computing teams compete on European Stage

NWERC 2017 Contestants
NWERC 2017 University Teams

On Sunday 26th November, two DCU computing undergraduate teams competed in the Northwestern Europe Regional Contest (NWERC) at University of Bath in the UK as part of the World ACM ICPC competition. Together with Irish undergraduate teams from Trinity, Maynooth and Queens they competed against university teams from 60 different institutions from 10 different countries in North West Europe.

The DCU team of ‘-= [B]ichael [B] [B]iggins =-’ won the ‘Irish’ title at the competition and placed an impressive 11th out of 42 UK and Irish teams, including teams from Cambridge and Oxford. The team consisted of Noah Donnelly (CPSSD3), Cian Ruane (CPSSD3) and Ciara Godwin (CASE3) solved 4 of the 12 algorithmic problems in the shortest time compared to other Irish teams and made attempts on all the problems. Overall they finished 47th out of the best 120 university teams across north Europe.

Silicon Republic article on DCU's Adapt centre's MT Evolution

Adapt LogoAdapt's Prof. Andy Way was recently interviewed by Silicon Republic on the MT evolution and it's application at the Adapt Centre, where Way and his team tackle language barriers that are “key challenges in enabling content to flow fluently across the globe”.

“From the late ’80s to about 2015, the dominant approach to MT was statistical (SMT). We needed large amounts of parallel data, ie source sentences and their human-provided translations, to build our statistical translation models, which essentially would suggest target-language words and phrases, which the model believed to be translations of the source sentence.

The last three years in MT have also seen neural MT (NMT) come to the fore. With NMT, all a research team needs is parallel data. The dominant model encodes the source sentence into a numerical vector representation, “which is in turn sent en bloc to the target-language decoder, whose job it is to generate the most likely target text from that vector”.

Way explained that NMT typically outperforms SMT and could be considered the “new state of the art”, citing more fluent translations and better word order as results. NMT does require much bigger training datasets, and models generally also take longer to train.

Full article: https://www.siliconrepublic.com/machines/machine-translation-adapt-dcu

ADAPT Innovation shortlisted for QS Reimagine Education Award

Adapt LogoAn ADAPT designed platform, DCU FUSE, was recently shortlisted for the prestigious QS Reimagine Education Award under the Ethical Leadership Category. DCU Fuse, an innovative initiative rolled out in Dublin City University earlier this year was aimed at informing the direction of the University’s next strategic plan through a 24 hour public brainstorming session supported by technology developed by researchers at the ADAPT Centre for Digital Content Technology.

Reimagine Education is a significant international competition, rewarding innovative initiatives aimed at enhancing student learning outcomes and employability. Each year, over 1000 educational innovators from all the world submit their projects to 16 Award Categories. The Ethical Leadership Award, sponsored by the World Economic Forum, is presented to the project most successful in fostering and encouraging leadership among students, and ensuring that students are aware of the ethical implications of leadership.

Syndicate content