Document-level translation quality estimation: exploring discourse and pseudo-references
By Carolina Scarton, Lucia Specia. 2014
31 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
- Ondrej Bojar
- Philipp Koehn
- Lucia Specia
- Barry Haddow
- Matt Post
- Christof Monz
- Yvette Graham
- Graham Neubig
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…”
- USHEF and USAAR-USHEF participation in the WMT15 QE shared task (2015, methodology): “We considered the discourse-aware features presented in Scarton and Specia (2014), which are already implemented in the QuEst framework (called herein as discourse repetition features): • word/lemma/noun repetition in the source/target document • ratio of word/lemma/noun repetition between source…”
- Searching for Context: a Study on Document-Level Labels for Translation Quality Estimation (2015, background): “Scarton and Specia (2014) propose a number of discourse-informed features in order to predict BLEU and TER at document level.”
- Multi-level Translation Quality Prediction with QuEst++ (2015, methodology): “Our document-level features follow from those in the work of (Wong and Kit, 2012) on MT evaluation and (Scarton and Specia, 2014) for document-level QE.”