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Edinburgh Neural Machine Translation Systems for WMT 16

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arxiv 1606.02891 v2 pith:2JH5GGXX submitted 2016-06-09 cs.CL

Edinburgh Neural Machine Translation Systems for WMT 16

Rico Sennrich , Barry Haddow , Alexandra Birch This is my paper
classification cs.CL
keywords systemstranslationenglishdirectionsimprovementsneuralnewsparticipated
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs, each trained in both directions: English<->Czech, English<->German, English<->Romanian and English<->Russian. Our systems are based on an attentional encoder-decoder, using BPE subword segmentation for open-vocabulary translation with a fixed vocabulary. We experimented with using automatic back-translations of the monolingual News corpus as additional training data, pervasive dropout, and target-bidirectional models. All reported methods give substantial improvements, and we see improvements of 4.3--11.2 BLEU over our baseline systems. In the human evaluation, our systems were the (tied) best constrained system for 7 out of 8 translation directions in which we participated.

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