LORIA System for the WMT15 Quality Estimation Shared Task

By David Langlois. 2015

11 citations recorded by the source corpus.

We describe our system for WMT2015 Shared Task on Quality Estimation, task 1, sentence-level prediction of post-edition effort. We use baseline features, Latent Semantic Indexing based features and features based on pseudo-references. SVM algorithm allows to estimate the linear regression between the features vectors and the HTER score. We use a selection algorithm in order to put aside needless features. Our best system leads to a performance in terms of Mean Absolute Error equal to 13.34 on official test while the official baseline system leads to a performance equal to 14.82.

Subfields: Artificial Intelligence, Machine Translation

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In Artificial Intelligence

1. Chris Hokamp
2. André F. T. Martins
3. Marcin Junczys-Dowmunt
4. Roman Grundkiewicz
5. Ramón Fernandez Astudillo
6. Fabio Kepler
7. Julia Kreutzer
8. Felix Stahlberg

In Machine Translation

1. Chris Hokamp
2. Marcin Junczys-Dowmunt
3. Roman Grundkiewicz
4. André F. T. Martins
5. Markus Freitag

Citations in the corpus

• Findings of the 2015 Workshop on Statistical Machine Translation (2015, neutral): “, 2015) LORIA Lorraine Laboratory of Research in Computer Science and its Applications, France (Langlois, 2015) RTM-DCU Dublin City University, Ireland (Bicici et al.”
• Pushing the Limits of Translation Quality Estimation (2017, background): “…we (Bicici et al., 2015; Kozlova et al., 2016) and in the sentence ranking track (Langlois, 2015; Kim and Lee, 2016). report the performance of the two best systems in the sentence-level QE tasks at WMT15 and WMT16 (Bicici et al., 2015; Langlois, 2015; Kozlova et al., 2016; Kim and Lee, 2016).”