Data enhancement and selection strategies for the word-level Quality Estimation
By Varvara Logacheva, Chris Hokamp, Lucia Specia. 2015
7 citations recorded by the source corpus.
This paper describes the DCU-SHEFF word-level Quality Estimation (QE) system submitted to the QE shared task at WMT15. Starting from a baseline set of features and a CRF algorithm to learn a sequence tagging model, we propose improvements in two ways: (i) by filtering out the training sentences containing too few errors, and (ii) by adding incomplete sequences to the training data to enrich the model with new information. We also experiment with considering the task as a classification problem, and report results using a subset of the features with Random Forest classifiers.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Varvara Logacheva
- Frédéric Blain
- Lucia Specia
- Desmond Elliott
- Ondrej Bojar
- Loïc Barrault
- Chris Hokamp
- Graham Neubig
Citations in the corpus
- Findings of the 2015 Workshop on Statistical Machine Translation (2015, neutral): “…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 Laboratory of Research in Computer Science and…”
- Phrase-level Quality Estimation for Machine Translation (2015, methodology): “Therefore, following [20] we use only sentences with the highest HTER score (i.”
- USFD’s Phrase-level Quality Estimation Systems (2016, methodology): “Previous experiments with word-level QE showed that the distribution of labels can be smoothed by filtering out sentences with little or no errors (Logacheva et al., 2015).”