[PDF] Top 20 A Stochastic Decoder for Neural Machine Translation

... eye implements several different NMT models but here we use the standard recurrent attentional model described in Section 2. We report baselines with and without dropout (Srivastava et al., 2014). For dropout a retention ... See full document

... the translation output, and we list the “Adaptive” results re- ported in their ...the translation quality purely by lookup from the phrase table, denoted as Phrase-Table Lookup, which serves as the ... See full document

... one decoder for many- to-one language ...multi-source translation problem, where the de- coder is ...encoder- decoder model to work on many-to-many languages trans- ...language translation, ... See full document

... inspired by multilingual NMT systems (Johnson et al., 2016). Instead of having multiple source and target languages, we used one source language and two target language variations. The training data consists of sequences ... See full document

... bi-scale decoder for neural machine translation, in which way, the target sentence is translated hierarchically from chunks to words, with information in different granularities being ... See full document

... chine translation has considered words as a ba- sic ...existing translation systems, such as language models and phrase tables, are a count-based estimator of ...phrase-based machine ... See full document

... for machine translation which allows paring various modern neural models of translation with different kinds of constraints and symbolic ...in Machine Learning, Speech and Language ... See full document

... convolutional neural machine translation without this bottleneck was first achieved in Kaiser and Bengio (2016) and Kalchbrenner et ...(Extended Neural GPU) used a recurrent stack of gated ... See full document

... Following the success of attentional NMT, a num- ber of approaches and models have been proposed for NMT recently, which can be grouped into differ- ent categories according to their motivations: deal- ing with rare ... See full document

... Each decoder block contains self- attention, followed by encoder-decoder attention, followed by two fully connected feed-forward layers with a ReLU between ... See full document

... Recently, with the success of Neural Machine Translation (NMT) models (Sutskever et al., 2014; Bahdanau et al., 2015), new kinds of APE methods have been proposed that use encoder-decoder ap- ... See full document

... One main drawback of this approach is that the whole source sentence has to be stored in a fixed- size context vector. To overcome this problem, (Bahdanau et al., 2014) introduced the soft attention mechanism. Instead of ... See full document

... the translation also depends on the professions from the Occupations test and its predicted ...and decoder sides, the model shows a higher accuracy when predict- ing the gender of a profession in ... See full document

... Neural machine translation (NMT) systems have recently achieved re- sults comparable to the state of the art on a few translation tasks, including English→French and ... See full document

... We also extend the decoder to incorporate infor- mation about the source syntax into the attention model. We have observed two issues in transla- tions produced using the tree encoder. First, a syn- tactic phrase ... See full document

... function is a highly non-convex function of the parameters with more complicated land- scape than that for decoder without exter- nal memory, rendering direct optimization over all the parameters rather difficult. ... See full document

... a translation, using the partially-generated tree to guide the translation process (§ ...to neural models of tree-structured data from syntactic and semantic parsing (Dyer et ... See full document

... A number of recent papers have proposed to use neural networks to directly learn the condi- tional distribution from a bilingual, parallel cor- pus (Kalchbrenner and Blunsom, 2013; Cho et al., 2014; Sutskever et ... See full document

... the decoder predicts the content word “ 帰っ (go back)”, it has to predict four function words “ て (suffix)”, “ しまい (perfect tense)”, “ たい (de- sire)”, and “ と (to)” before predicting the next content word “ 思っ ... 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