Findings of the 2016 Conference on Machine Translation (WMT16) Bojar, O.; Chatterjee, R.; Federmann, C.; Graham, Y.; Haddow, B.; Huck, M.; Jimeno Yepes, A.; Koehn, P.; Logacheva, V.; Monz, C.; Negri, M.; Névéol, A.; Neves, M.; Popel, M.; Post, M.; Rubino, R.; Scarton, C.; Specia, L.; Turchi, M.; Verspoor, K.; Zampieri, M.Abstract This paper presents the results of the WMT16 shared tasks, which included five machine translation (MT) tasks (standard news, IT-domain, biomedical, multimodal, pronoun), three evaluation tasks (metrics, tuning, run-time estimation of MT quality), and an automatic post-editing task and bilingual document alignment task. 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. The IT-domain task received 31 submissions from 12 institutions in 7 directions and the Biomedical task received 15 submissions systems from 5 institutions. Evaluation was both automatic and manual (relative ranking and 100-point scale assessments).The quality estimation task had three subtasks, with a total of 14 teams, submitting 39 entries. The automatic post-editing task had a total of 6 teams, submitting 11 entries.1 http://statmt.org/wmt16/results.html 2

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This paper presents the results of the WMT14 shared tasks, which included a standard news translation task, a separate medical translation task, a task for run-time estimation of machine translation quality, and a metrics task. This year, 143 machine translation systems from 23 institutions were submitted to the ten translation directions in the standard translation task. An additional 6 anonymized systems were included, and were then evaluated both automatically and manually. The quality estimation task had four subtasks, with a total of 10 teams, submitting 57 entries.

Differently from the phrase-based paradigm, neural machine translation (NMT) operates on word and sentence representations in a continuous space. This makes the decoding process not only more difficult to interpret, but also harder to influence with external knowledge. For the latter problem, effective solutions like the XML-markup used by phrase-based models to inject fixed translation options as constraints at decoding time are not yet available. We propose a "guide" mechanism that enhances an existing NMT decoder with the ability to prioritize and adequately handle translation options presented in the form of XML annotations of source words. Positive results obtained in two different translation tasks indicate the effectiveness of our approach.

We present the results from the 5 th round of the WMT task on MT Automatic Post-Editing. The task consists in automatically correcting the output of a "black-box" machine translation system by learning from human corrections. Keeping the same general evaluation setting of the previous four rounds, this year we focused on two language pairs (English-German and English-Russian) and on domain-specific data (Information Technology). For both the language directions, MT outputs were produced by neural systems unknown to participants. Seven teams participated in the English-German task, with a total of 18 submitted runs. The evaluation, which was performed on the same test set used for the 2018 round, shows further progress in APE technology: 4 teams achieved better results than last year's winning system, with improvements up to-0.78 TER and +1.23 BLEU points over the baseline. Two teams participated in the English-Russian task submitting 2 runs each. On this new language direction, characterized by a higher quality of the original translations, the task proved to be particularly challenging. Indeed, none of the submitted runs improved the very high results of the strong system used to produce the initial translations (16.16 TER, 76.20 BLEU).