Type

Conference Proceedings

Authors

Andy Way
Qun Liu
Mehmet Ergun Bicici

Subjects

Linguistics

Topics
rtm machine learning machine translating translation quality parfda referential translation machines model selection information retrieval

Referential translation machines for predicting translation quality and related statistics (2015)

Abstract We use referential translation machines (RTMs) for predicting translation performance. RTMs pioneer a language independent approach to all similarity tasks and remove the need to access any task or domain specific information or resource. We improve our RTM models with the ParFDA instance selection model (Bicici et al., 2015), with additional features for predicting the translation performance, and with improved learning models. We develop RTM models for each WMT15 QET (QET15) subtask and obtain improvements over QET14 results. RTMs achieve top performance in QET15 ranking 1st in document- and sentence-level prediction tasks and 2nd in word-level prediction task.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: Centre for Next Generation Localisation (CNGL)
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating
Ireland -> Dublin City University -> Subject = Computer Science: Information retrieval
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

Andy Way, Qun Liu, Mehmet Ergun Bicici

Experts in our system

1
Andy Way
Dublin City University
Total Publications: 229
 
2
Qun Liu
Dublin City University
Total Publications: 31