[PDF] Top 20 Bridging the Gap between Training and Inference for Neural Machine Translation

... In training the NMT model, we limit the source and target vocabulary to the most frequent 30K words for both sides in the Zh → En translation task, covering approximately ...De translation task, ... 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

... 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

... the neural network to learn good hidden representations all by ...efficient inference of the NAT ...model training. First, to overcome the problem of repeated translation, we propose to force ... See full document

... custom inference engine, Amun (Junczys- Dowmunt et ...deep-learning inference is an is- sue that deserves dedicated tools that are not com- promised by competing objectives such as training or ... See full document

... NMT translation, in our new bridging approach we do not use extra re- sources in the NMT model, but let the model itself learn the similarity of word pairs from the training ... See full document

... The goal of the experiments described here was to predict the most probable sequence of non-manual features for a sequence of glosses output by the machine translation system (cf. Figure 1). As stated in ... See full document

... dimensional neural network used by White et ...FN+’s training set. Since FN+ tests pa- raphrastic inference and NMT models have been shown to be useful to generate sentential paraphra- se pairs ... See full document

... into neural machine trans- lation at run ...the inference step, and, as we show in this paper, can be brittle when tested in realistic ...by training a neural MT system to learn how to ... See full document

... This is the second time for SRCB to participate in WAT. This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019. We participated in ASPEC tasks ... See full document

... challenging. Training from scratch on each new domain is im- practical, while continuing training on a new do- main can cause catastrophic forgetting of previous tasks (French, 1999), even in an ensemble ... See full document

... a target word if its probability does not decrease after a READ operation. The Wait-if-Diff (WID) agent instead WRITES a target word if the target word remains unchanged after a READ operation. Static Read and Write: The ... See full document

... dependency between words in a target sentence. This limitation in training process creates fundamental barrier of represent- ing correct translation process in a neural net- ...dependency ... See full document

... the training sets seen in Table 1. Graphical training histories are shown in Figure 1, summarized in Table ...“Student” training data set both trains the fastest and leads to the highest- scoring ... See full document

... representations between the languages in Figure 2 (right) (in the Spanish decoder visualiza- ...different translation perfor- mance, further research is required to understand the correlation between ... See full document

... highlighting the prominent gap between Transport availability and its safety in the African Sub-Continent. To begin with, the initial data regarding the current situation of Transportation in Africa were ... See full document

... this translation contains gender bias since it ig- nores the fact that, for both cases, “friend” is a female and translates by focusing on the occupa- tional stereotypes, ... See full document

... 44 Direct comparisons of rural and urban roles are lacking in the paramedic literature, but not so with literature from other health disciplines. Alford and O’Meara (2001) for example, compared the roles of rural and ... See full document

... Neural Statistical Machine Translation Neural Machine Translation Encoder Decoder Sequence-to-sequence learning: Encoder Sequence-to-sequence learning: Decoder Let’s use a simple NN for [r] ... See full document

... The description in Table 1 shows that all the three groups of experts obtain consensus for the principles such as self-critique principles, conceptual principles, marketability principles, consistency principles, ... See full document