[PDF] Top 20 Supervised Attentions for Neural Machine Translation

... of neural machine trans- lation by utilizing the alignments of train- ing sentence ...the machine attentions and the “true” alignments, and minimize this cost in the training ... See full document

... Neural machine translation (NMT) (Cho et ...alignments, translation rules, and complicated de- coding algorithms, which are the characteristics of phrase-based statistical machine ... See full document

... Artetxe et al. (2017) built upon described methods to train translation model without any parallel corpora at all. They trained a shared encoder which should encode sentences into the language-agnostic ... See full document

... In this paper, we described our NMT system, which is based on Transformer model. We made several changes to original Transformer model, including relative position representation, deep layer model, ensembling and other ... See full document

... 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

... 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

... While all of the prior work described in Section 2 is relatively recent, non-autoregressive methods for decoding in NMT have been around for longer, al- though none relies on syntax like SynST. Schwenk (2012) translate ... See full document

... Many recent works have led to notable improvements in the attention mechanism for neural machine translation. Tu et al. (2016) introduced an explicit coverage vector into the attention mechanism to ... See full document

... number of accepted sentences increases through- out the epochs, and so does the number of unique sentences used in training. Especially the first it- eration over the data set is vital for improving and adapting the ... 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 ... See full document

... 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

... Human-Informed Translation and Interpreting Technology (HiT-IT 2019) took place in Varna, Bulgaria and spanned over two days (5-6 September 2019), as a post-RANLP 2019 conference ... See full document

... English translation tasks in both direction. We have used the neural machine translation systems with attention model and utilized the UCSY-corpus and ALT ...the translation systems can ... See full document

... convolutional neural networks are designed and trained with different hyper- parameter values, changing behavior of the training curves are analyzed and the one with best evaluation metrics is ... See full document

... First of all, we evaluate the overall translation accuracy of each NMT system and production system on the concatenated data (ITEM+TEXT). Table 2 reports the case-sensitive sacreBLEU scores of the NMT systems and ... See full document

... 2) Linear Combiner - All the inputs are fed to the neuron; in addition to these input values each link in neural network has an associated weight parameter “wi”. The summation unit of neuron initially finds the ... See full document

... two translation di- rections are not considered as equivalent: one speaks about forward transliteration (for example, transliteration of an Arabic name into the Latin alphabet) and backward translit- eration ... See full document

... the translation of n-grams that do occur in the train- ing set but are infrequent (occur less than 5 ...collected translation pieces based on sen- tence similarities and does not prefer more fre- quent ... See full document

... Finally, we perform a full comparison between the various methods for integrating lexicons into the translation process, with results shown in Table 4. In general the bias method improves accuracy for the auto and ... See full document

... The network architectures of NMT models are simple but effective. It produces a sentence represen- tation with the encoder, then the decoder generates the translation from the vector of sentence repre- sentation. ... See full document