The JHU Machine Translation Systems for WMT 2016

By Shuoyang Ding, Kevin Duh, Huda Khayrallah, Philipp Koehn, Matt Post. 2016

20 citations recorded by the source corpus.

This paper describes the submission of Johns Hopkins University for the shared translation task of ACL 2016 First Conference on Machine Translation (WMT 2016). We set up phrase-based, hierarchical phrase-based and syntax-based systems for all 12 language pairs of this year’s evaluation campaign. Novel research directions we investigated include: neural probabilistic language models, bilingual neural network language models, morphological segmentation, and the attentionbased neural machine translation model as reranking feature.

Subfields: Artificial Intelligence

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Citations in the corpus

Findings of the 2016 Conference on Machine Translation (2016, neutral): “JHU-* Johns Hopkins University (Ding et al., 2016)”
Six Challenges for Neural Machine Translation (2017, methodology): “We build phrase-based systems using standard features that are commonly used in recent system submissions to WMT (Williams et al., 2016; Ding et al., 2016a).”
On the Impact of Various Types of Noise on Neural Machine Translation (2018, methodology): “4 We build phrase-based systems using standard features commonly used in recent system submissions to WMT (Haddow et al., 2015; Ding et al., 2016, 2017).”
Neural Interactive Translation Prediction (2016, unknown): “The system matches Johns Hopkins University’s submission to the WMT shared task (Ding et al., 2016).”
Neural Machine Translation (2020, unknown): “We build phrase-based systems using standard features that are commonly used in recent system submissions to WMT (Williams et al., 2016; Ding et al., 2016).”