mlsanityData to 7 Oct 2026

Edinburgh Neural Machine Translation Systems for WMT 16

arXiv 1606.02891 ↗First posted 9 Jun 2016cs.CLcited by 76

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Abstract (arXiv)

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.

Key facts. Cited by 76 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2016.

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Citing papers per year: 2021: 25; 2022: 14; 2023: 13; 2024: 14; 2025: 6; 2026: 4.

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