[PDF] Top 20 The Helsinki Neural Machine Translation System

... final system tested after the official submission is an en- semble of four independently trained models with savepoint averaging over the last four savepoints and reaches a lowercased BLEU score of ... See full document

... Recently, neural machine translation (NMT) has gained popularity in the field of machine trans- ...recurrent neural networks (RNN); one is an en- coder, which encodes a source sequence ... See full document

... the system to pay much attention on them can adjust it towards “mastering” more information for these ...NMT system with 80% of the most complex ...the system per- forms better in terms of accuracy ... See full document

... How can we define lexical units to maximize information sharing across related source lan- guages? We compare different configurations of sub-word segmentations using different variants of the standard Byte-Pair Encoding ... See full document

... while the TEXT datasets does 28,507. These dif- ferent trends between the ITEM and TEXT data suggest that the systems are required to translate relatively fixed phrases or sentences more in the ITEM data set, which is a ... See full document

... statistical machine transla- tion (SMT) that typically has a huge phrase/rule ta- ...SMT system, NMT has recently attracted growing inter- ests from both deep learning and machine transla- tion ... See full document

... existing machine trans- lation pipelines (Chan et ...overall translation performance as measured by ...improve translation quality and accuracy of am- biguous ... See full document

... Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan ... See full document

... As can be seen from Table 7, for the English↔X directions, all three models show comparable scores. On the Ru↔{Be, Uk} directions, the “Zero-Shot” model already achieves relatively high BLEU scores for all directions ... See full document

... Although English(En) and Hindi(Hi) lan- guages belong to the same family (Indo- European), they differ significantly in terms of word order, syntax and morphological struc- ture (Bharati et al., 1995). While English ... See full document

... the translation industry. Nowadays, translation companies must be more competitive than ever and meet fast commercial ...time. Machine translation (MT) can help them to achieve this goal: ... See full document

... shared task (Bojar et al., 2019). We par- ticipate in Gujarati-English language pair and submit two systems: English → Gujarati and Gujarati → English. Gujarati language belongs to Indo-Aryan language family and is ... See full document

... Language Translation task in WMT ...a neural machine transla- tion system which share encoder and have separate decoders for each language, in a semi-supervised ... See full document

... continuous-valued numbers. This is in contrast to more traditional SMT methods such as phrase-based machine translation (PBMT; Koehn et al. (2003)), which represent translations as discrete pairs of word ... See full document

... • misinterpretation of a named entity as a com- mon noun. E.g., Dane Whitman translated as датчанин Уитмен (‘inhabitant of Denmark Whitman’) instead of Дейн Уитмен. It should be noted that since the Edinburgh NMT ... See full document

... statistical machine translation, where it has been used for semi-supervised learning (Bojar and Tam- chyna, 2011), or self-training (Goutte et ...their system de- scription iteratively applying ... See full document

... We compare different monolingual corpus sizes for the German → English task in Figure 2 on three different test sets. Particularly, newstest2018 shows the exact opposite behaviour from the re- maining test sets: the ... See full document

... In our experiments, we used up to six encoder-decoder NMT models (three pairs); English→French, French→English, English→Czech, Czech→English, English→Ger- man, German→English. All systems were trained on the available ... See full document

... We used 1 million sentence pairs that are con- tained in train-1.txt of ASPEC-JE corpus for training the translation rule tables and NMT models. We also used 3 million Japanese sen- tences that are contained in ... See full document

... monolingual data (i.e. DCEP, DGT, EAC, ECDC) for back-translation. In all our experiments, we observed an increase in BLEU score for the generic test set when adding synthetic data to the parallel training corpus. ... See full document