Recurrent Neural Network based Translation Quality Estimation
By Hyun Kim, Jong-Hyeok Lee. 2016
34 citations recorded by the source corpus.
This paper describes the recurrent neural network based model for translation quality estimation. Recurrent neural network based quality estimation model consists of two parts. The first part using two bidirectional recurrent neural networks generates the quality information about whether each word in translation is properly translated. The second part using another recurrent neural network predicts the final quality of translation. We apply this model to sentence, word and phrase level of WMT16 Quality Estimation Shared Task. Our results achieve the excellent performance especially in sentence and phraselevel QE.
Subfields: Artificial Intelligence, Information Systems, Machine Translation
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
- Philipp Koehn
- Ondrej Bojar
- Lucia Specia
- Matt Post
- Graham Neubig
- Felix Stahlberg
- Barry Haddow
- Markus Freitag
In Information Systems
- Philipp Koehn
- Matt Post
- Christof Monz
- Lucia Specia
- Ondrej Bojar
- Barry Haddow
- Yvette Graham
- Matthias Huck
In Machine Translation
- Philipp Koehn
- Lucia Specia
- Matt Post
- Ondrej Bojar
- Varvara Logacheva
- Barry Haddow
- Christian Federmann
- Matteo Negri
Citations in the corpus
- Findings of the 2017 Conference on Machine Translation (WMT17) (2017, background): “POSTECH (T1, T2, T3): POSTECH’s submissions to the sentence/word/phrase-level QE tasks are based on predictor-estimator architecture (Kim et al., 2017; Kim and Lee, 2016), which is the two-stage end-to-end”
- Pushing the Limits of Translation Quality Estimation (2017, methodology): “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).”