Searching for Context: a Study on Document-Level Labels for Translation Quality Estimation
By Carolina Scarton, Marcos Zampieri, Mihaela Vela, Josef van Genabith, Lucia Specia. 2015
27 citations recorded by the source corpus.
Subfields: Natural Language Processing, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Natural Language Processing
- Lucia Specia
- Ondrej Bojar
- Philipp Koehn
- Barry Haddow
- Christof Monz
- Matt Post
- Varvara Logacheva
- Yvette Graham
In Evaluation & Benchmarks
- Ondrej Bojar
- Lucia Specia
- Philipp Koehn
- Yvette Graham
- Matt Post
- Barry Haddow
- Christian Federmann
- Marco Turchi
Citations in the corpus
- Findings of the 2016 Conference on Machine Translation (2016, methodology): “The discourse aware features are the same as the ones used by Scarton et al. (2015a) plus Latent Semantic Analysis (LSA) cohesion features (Scarton and Specia, 2014), number of subtrees and height of the Rhetorical Structure Theory (RST) tree and entity graph-based coherence scores (Sim Smith et…”
- Findings of the 2015 Workshop on Statistical Machine Translation (2015, methodology): “The network was pre-trained by initialising the word lookup-table with distributed word representations, ID Participating team DCU-SHEFF Dublin City University, Ireland and University of Sheffield, UK (Logacheva et al., 2015) HDCL Heidelberg University, Germany (Kreutzer et al., 2015) LORIA Lorraine ”
- Word embeddings and discourse information for Quality Estimation (2016, methodology): “Finally, Scarton (2015), Scarton and Specia (2015) and Scarton et al. (2015b) analyse the task of document-level QE from the perspective of defining reliable labels.”
- USHEF and USAAR-USHEF participation in the WMT15 QE shared task (2015, background): “Other discourse features were also explored (following the work of Scarton (2015)): • number of pronouns in the source/target document • number of discourse connectives in the source/target document (Charniak, 2000) (we count the P RP tags).”
- Multi-level Translation Quality Prediction with QuEst++ (2015, methodology): “In addition, no datasets with human-created labels are available and thus scores produced by automatic metrics have to be used as approximation (Scarton et al., 2015).”