Results of the WMT16 Tuning Shared Task
By Bushra Jawaid, Amir Kamran, Miloš Stanojević, Ondrej Bojar. 2016
7 citations recorded by the source corpus.
This paper presents the results of the WMT16 Tuning Shared Task. We provided the participants of this task with a complete machine translation system and asked them to tune its internal parameters (feature weights). The tuned systems were used to translate the test set and the outputs were manually ranked for translation quality. We received 4 submissions in the Czech-English and 8 in the English-Czech translation direction. In addition, we ran 2 baseline setups, tuning the parameters with standard optimizers for BLEU score. In contrast to previous years, the tuned systems in 2016 rely on large data.
Subfields: Artificial Intelligence, 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 Artificial Intelligence
- Graham Neubig
- Philipp Koehn
- Ondrej Bojar
- Lucia Specia
- Felix Stahlberg
- Matt Post
- Luke S. Zettlemoyer
- Rico Sennrich
In Evaluation & Benchmarks
- Ondrej Bojar
- Lucia Specia
- Philipp Koehn
- Matt Post
- Yvette Graham
- Barry Haddow
- Graham Neubig
- Christian Federmann
Citations in the corpus
- Findings of the 2016 Conference on Machine Translation (2016, methodology): “This year, 102 MT systems from 24 institutions (plus 36 anonymized online systems) were submitted to the 12 translation directions in the news translation task.”
- Beyond BLEU:Training Neural Machine Translation with Semantic Similarity (2019, methodology): “The WMT shared tasks included tunable metric tasks in 2011 (Callison-Burch et al., 2011) and again in 2015 (Stanojevi´c et al., 2015) and 2016 (Jawaid et al., 2016).”