[PDF] Top 20 Self Supervised Neural Machine Translation
Has 10000 "Self Supervised Neural Machine Translation" found on our website. Below are the top 20 most common "Self Supervised Neural Machine Translation".
Self Supervised Neural Machine Translation
... a neural architecture is exhausted, more data does not improve the ...This self-supervised architecture not only selects the data but it does it in the most useful way for the ... See full document
7
Self Attentive Residual Decoder for Neural Machine Translation
... We now examine the two scoring functions that can be used for the self-attentive residual con- nections model presented in Eq. (12), considering English-to-Chinese and Spanish-to-English. The BLEU scores are ... See full document
14
On the use of BERT for Neural Machine Translation
... convolutional self attention network (Yang et al., 2019). Using convolutional self attention network in BERT could bring additional benefit for the pretrained ... See full document
10
Lightly Supervised Transliteration for Machine Translation
... Arbabi et al. (1994) present a hybrid algorithm for romanization of Arabic names using neural networks and a knowledge based system. The pro- gram applies vowelization rules, based on Arabic morphology and ... See full document
9
Iterative Back Translation for Neural Machine Translation
... statistical machine translation, where it has been used for semi-supervised learning (Bojar and Tam- chyna, 2011), or self-training (Goutte et ... See full document
7
Multi Granularity Self Attention for Neural Machine Translation
... Multi-Head Attention Multi-head attention mechanism has shown its effectiveness in machine translation (Vaswani et al., 2017) and generative dialog (Tao et al., 2018) systems. Recent studies shows that the ... See full document
11
Mixed Multi Head Self Attention for Neural Machine Translation
... Neural machine translation must consider the correlated ordering of words, where order has a lot of influence on the meaning of a sen- tence (Khayrallah and Koehn, ...on translation task, ... See full document
9
Supervised neural machine translation based on data augmentation and improved training & inference process
... of neural networks in machine translation has brought great improvement on translation quality over traditional statistical machine translation (SMT) in recent years ... See full document
5
Combining Translation Memory with Neural Machine Translation
... (2017), the inputs are mapped into the 512- dimensional embedding space with positional em- bedding. Both the encoder and decoder networks map the vectors through 6-layer 2048-dimensional feed-forward networks with ... See full document
8
Why Self Attention? A Targeted Evaluation of Neural Machine Translation Architectures
... lutional, self-attentional) have outperformed RNNs in neural machine ...and self-attentional networks can connect dis- tant words via shorter network paths than RNNs, and it has been ... See full document
10
NICT Self Training Approach to Neural Machine Translation at NMT 2018
... The self-training approach in this study is based on a method proposed by Imamura et ...forward translation model is trained using the orig- inal and synthetic parallel ...the translation quality im- ... See full document
6
Scaling Neural Machine Translation
... a self- attention layer, followed by two fully connected feed-forward layers with a ReLU non-linearity between ...contains self- attention, followed by encoder-decoder attention, followed by two fully ... See full document
9
Neural Machine Translation with Supervised Attention
... a neural network method to learn a BTG reordering ...some neural network based reordering models, such as (Zhang et ...recurrent neural network based word-level reordering ...and translation ... See full document
10
Semi Supervised Neural Machine Translation with Language Models
... Training neural machine translation models is notoriously slow and requires abundant paral- lel corpora and computational ...to translation systems, also we investigate several techniques to ... See full document
8
Semi Supervised Learning for Neural Machine Translation
... Figure 2 shows the BLEU scores of various set- tings of k over time. Only the English mono- lingual corpus is appended to the training data. We observe that increasing the size of the approx- imate search space generally ... See full document
10
Supervised Attentions for Neural Machine Translation
... Yang Liu, Haitao Mi, Yang Feng, and Qun Liu. 2009. Joint decoding with multiple translation models. In Proceedings of the Joint Conference of the 47th An- nual Meeting of the ACL and the 4th International Joint ... See full document
6
Variational Neural Machine Translation
... semi- supervised learning with generative models and fur- ther develop new models that allow effective gen- eralization from a small labeled dataset to a large unlabeled ...recurrent neural network, while ... See full document
10
Syntactically Supervised Transformers for Faster Neural Machine Translation
... We evaluate the translation quality (in terms of BLEU) and the decoding speedup (average time to decode a sentence) of SynST compared to com- peting approaches. In a controlled series of experi- ments on four ... See full document
13
NICT’s Supervised Neural Machine Translation Systems for the WMT19 News Translation Task
... We used Marian toolkit (Junczys-Dowmunt et al., 2018) 13 to build competitive NMT systems based on the Transformer (Vaswani et al., 2017) archi- tecture. We used the byte pair encoding (BPE) algorithm (Sennrich et al., ... See full document
7
Incorporating Discrete Translation Lexicons into Neural Machine Translation
... 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
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