Type

Conference Proceedings

Authors

Lucia Specia
Jennifer Foster
Jose de Souza
Raphael Rubino

Subjects

Linguistics

Topics
topic computational linguistics machine learning translation quality state of the art quality estimation machine translating machine translation

Topic models for translation quality estimation for gisting purposes (2013)

Abstract This paper addresses the problem of predicting how adequate a machine translation is for gisting purposes. It focuses on the contribution of lexicalised features based on different types of topic models, as we believe these features are more robust than those used in previous work, which depend on linguistic processors that are often unreliable on automatic translations. Experiments with a number of datasets show promising results: the use of topic models outperforms the state-of-the-art approaches by a large margin in all datasets annotated for adequacy.
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 -> Subject = Computer Science
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating
Ireland -> Dublin City University -> Subject = Computer Science: Computational linguistics
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing
Ireland -> Dublin City University -> Subject = Computer Science: Machine learning

Full list of authors on original publication

Lucia Specia, Jennifer Foster, Jose de Souza, Raphael Rubino

Experts in our system

1
Jennifer Foster
Dublin City University
Total Publications: 53
 
2
Raphael Rubino
Dublin City University
Total Publications: 8