[PDF] Top 20 Improving Low Resource Machine Translation using Morphological Glosses (Non archival Extended Abstract)

... the morphological grammar of the highly-inflected source language to generate multi-word English ...these glosses, which better mimic in-situ translations, are more effective than dictionary entries when ... See full document

... To extend the training corpus, we used an OPUS English-Arabic corpus, which contains 11 million sentences, to generate a synthetic Korean-Arabic corpus. OPUS was used differently depending on whether it was used for the ... See full document

... data using the Buckwalter translitera- tion ...done using the Adam opti- mizer (Kingma and Ba, 2015), a learning rate of 10 −4 , and dropout of ...coded using a beam of 5 with length normalization ... See full document

... Figure 2 shows the average percentage of block usage for each task in an MTL model with 3 shared blocks, on the English-Farsi test set. We have ag- gregated the output of the routing network for the blocks in the encoder ... See full document

... So far, the constructed G is still confused by noise because the effect of noise has not been fully u- tilized due to the lack of attention from D. To solve this problem, we add a VIN implemented value screener between G ... See full document

... improve low-resource neural machine translation (NMT), the synthetic bilingual cor- pora generated by NMT models trained on limited authentic bilingual data are inevitably ...English-German ... See full document

... pervised machine translation where authors have shown that, up to a certain amount of bitext, bet- ter translation systems can be trained with these unsupervised approaches than supervised methods ... See full document

... the low-resource NMT to explicitly utilize the source-side linguistic knowledge, which models the word sequence in parallel to the linguistic features by using two separate encoders with parameter ... See full document

... chine translation (MT), especially in low-resource settings where neural MT systems often do not compete with phrase-based and syntax-based MT (Koehn and Knowles, ...data using the Expectation ... See full document

... Choi, Gyu Hyeon, Jong Hun Shin, and Young Kil Kim. 2018. Improving a Multi-Source Neural Machine Translation Model with Corpus Extension for Low-Resource Languages. In chair), Nicoletta ... See full document

... correct translation of the Japanese monolingual sentences, eventually be- came a good translation, we can say that the Japanese→Russian and Russian→Japanese mod- els used to create a pseudo-parallel corpus ... See full document

... Almahairi et al. (2016) produce the first results of neural machine translation on Arabic. They find that preprocessing of Arabic as used in sta- tistical machine translation is helpful. They ... See full document

... Most of the research on alignment combination in the past has focused on how to combine the alignments from two different directions, source- to-target and target-to-source. Usually people start from the intersection of ... See full document

... for improving machine translation from a resource-poor language to X by adapt- ing a large bi-text for a related resource-rich language and X (the same target ...the resource- ... See full document

... in low-data settings, and can outper- form PBSMT with far less parallel training data than previously ...in low-resource MT research has been the bet- ter exploitation of monolingual and multilingual ... See full document

... subsequent translation into Czech is achieved using two phrase-based translation sys- ...trained using English and Czech sentence– aligned parallel training data with no ...trained ... See full document

... neural machine translation (NMT) has recently proven success- ful (Eriguchi et ...done using an outside parser to syntactically annotate the training data, making this technique difficult to use for ... See full document

... novel morphological analysis technique which induces a morphological and syntactic symmetry between two languages with highly asymmetrical morphological structures to improve statistical ... See full document

... The bottom part of Table 4 shows a particu- larly interesting trick: the parent is not any high- resource pair but the very same EN-ET corpus with source and target swapped. We see gains in both directions, ... See full document

... Neural Machine Translation We propose a Universal NMT system that is fo- cused on the scenario where minimal parallel sen- tences are ...and low-resource ...the low-resource ... See full document