The UU Submission to the Machine Translation Quality Estimation Task
By Oscar Sagemo, Sara Stymne. 2016
6 citations recorded by the source corpus.
This paper outlines the UU-SVM system for Task 1 of the WMT16 Shared Task in Quality Estimation. Our system uses Support Vector Machine Regression to investigate the impact of a series of features aiming to convey translation quality. We propose novel features measuring reordering and noun translation errors. We show that we can outperform the baseline when we combine it with a subset of our new features.
Subfields: Artificial Intelligence, Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Information Systems
Citations in the corpus
- Predicting Translation Performance with Referential Translation Machines (2017, background): “• proportion of alignments that cross ( ≮ ) the link (Sagemo and Stymne, 2016) of any other alignments: • word alignment correspondence features (Sagemo and Stymne, 2016).”