Y
Yvette Graham
Researcher at Trinity College, Dublin
Publications - 80
Citations - 4172
Yvette Graham is an academic researcher from Trinity College, Dublin. The author has contributed to research in topics: Machine translation & Computer science. The author has an hindex of 27, co-authored 67 publications receiving 3327 citations. Previous affiliations of Yvette Graham include Dublin City University & University of Melbourne.
Papers
More filters
Proceedings ArticleDOI
Findings of the 2016 Conference on Machine Translation
Ondˇrej Bojar,Rajen Chatterjee,Christian Federmann,Yvette Graham,Barry Haddow,Matthias Huck,Antonio Jimeno Yepes,Philipp Koehn,Varvara Logacheva,Christof Monz,Matteo Negri,Aurélie Névéol,Mariana Neves,Martin Popel,Matt Post,Raphael Rubino,Carolina Scarton,Lucia Specia,Marco Turchi,Karin Verspoor,Marcos Zampieri +20 more
TL;DR: The results of the WMT16 shared tasks are presented, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task.
Proceedings ArticleDOI
Findings of the 2019 Conference on Machine Translation (WMT19)
Loïc Barrault,Ondřej Bojar,Marta R. Costa-jussà,Christian Federmann,Mark Fishel,Yvette Graham,Barry Haddow,Matthias Huck,Philipp Koehn,Shervin Malmasi,Christof Monz,Mathias Müller,Santanu Pal,Matt Post,Marcos Zampieri +14 more
TL;DR: This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2019, asked to build machine translation systems for any of 18 language pairs, to be evaluated on a test set of news stories.
Proceedings ArticleDOI
Findings of the 2018 Conference on Machine Translation (WMT18)
Ondřej Bojar,Christian Federmann,Mark Fishel,Yvette Graham,Barry Haddow,Philipp Koehn,Christof Monz +6 more
TL;DR: This paper presents the results of the premier shared task organized alongside the Conference on Machine Translation (WMT) 2018, asked to build machine translation systems for any of 7 language pairs in both directions, to be evaluated on a test set of news stories.
Proceedings ArticleDOI
Findings of the 2017 Conference on Machine Translation (WMT17)
Ondřej Bojar,Rajen Chatterjee,Christian Federmann,Yvette Graham,Barry Haddow,Shujian Huang,Matthias Huck,Philipp Koehn,Qun Liu,Varvara Logacheva,Christof Monz,Matteo Negri,Matt Post,Raphael Rubino,Lucia Specia,Marco Turchi +15 more
TL;DR: The results of the WMT17 shared tasks, which included three machine translation (MT) tasks (news, biomedical, and multimodal), two evaluation tasks (metrics and run-time estimation of MT quality), an automatic post-editing task, a neural MT training task, and a bandit learning task are presented.
Journal ArticleDOI
Can machine translation systems be evaluated by the crowd alone
TL;DR: A new methodology for crowd-sourcing human assessments of translation quality is presented, which allows individual workers to develop their own individual assessment strategy and has a substantially increased ability to identify significant differences between translation systems.