[PDF] Top 20 Multi Source Neural Translation
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Multi Source Neural Translation
... first translation by hand, then turns the rest over to machine translation ...The translation system now has two strings as input, which can reduce ambiguity via “triangu- lation” (Kay’s ... See full document
5
Mixed Multi Head Self Attention for Neural Machine Translation
... 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, resulting in a decrease ... See full document
9
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings
... For the efficient training of the neural net- works, we limited the source (Chinese) and target (English) vocabularies to the most frequent 30k words, covering approximately 97.7% and 99.3% of the two ... See full document
10
Document Level Information as Side Constraints for Improved Neural Patent Translation
... We used the Nematus NMT system 5 (Sennrich et al., 2017) to train an attentional encoder- decoder network (Bahdanau et al., 2015). The model parameters were optimized with ADADELTA (Zeiler, 2012), using a maximum ... See full document
12
Low Resource Corpus Filtering Using Multilingual Sentence Embeddings
... open source release 6 of the Zipporah tool without ...(probabilistic translation dic- tionaries and language models) were trained on the provided clean data (excluding the dictionar- ...lexical ... See full document
6
A Multi Hop Attention for RNN based Neural Machine Translation
... the multi-hop attention mechanism to the Transformer and reported that the Transformer augmented with the multi-hop attention mechanism significantly outperformed the ...to neural machine ... See full document
8
Multi Domain Neural Machine Translation through Unsupervised Adaptation
... the source sentence word by word into a sequence of hidden states; then, an- other recurrent neural network decodes the source hidden sequence into the target ...a translation requires sam- ... See full document
11
Multi Granularity Self Attention for Neural Machine Translation
... Representation Multi- granularity representation, which is proposed to make full use of subunit composition at different levels of granularity, has been explored in various NLP tasks, such as paraphrase ... See full document
11
CUNI System for the WMT17 Multimodal Translation Task
... Multimodal Translation Task. For Task 1 (multimodal translation), our best scoring system is a purely textual neural translation of the source image cap- tion to the target ... See full document
8
A Multi Task Architecture on Relevance based Neural Query Translation
... applying multi- tasking we train the transformer to obtain a rea- sonable MAP on the Val ...our multi-task transformer from that point, also con- tinuing to train the ...the multi-tasking model at ... See full document
6
Unsupervised Source Hierarchies for Low Resource Neural Machine Translation
... jecting source syntax into NMT requires parsing the training data with an external parser, and such parsers may be unavailable for low-resource lan- ...syntactic source information may improve low-resource ... See full document
7
Encoding Source Language with Convolutional Neural Network for Machine Translation
... proposed neural network joint model (NNJM) (Devlin et ...sen source context window, achieving state-of-the-art performance in ...relevant source information through a convolutional architecture ... See full document
11
Target Foresight Based Attention for Neural Machine Translation
... in neural machine translation, an attention model is used to identify the aligned source words for a target word target foresight word in order to select translation con- text, but it does not ... See full document
11
Multi Way, Multilingual Neural Machine Translation with a Shared Attention Mechanism
... based neural machine translation has become de facto standard in neural translation literatures re- cently (Jean et ...report translation perplex- ity, which is not a widely used metric ... See full document
10
Neural vs Phrase Based Machine Translation in a Multi Domain Scenario
... machine translation systems have recently outperformed their conventional statistical coun- terparts in the translation tasks in several domains such as news (Sennrich et ... See full document
5
On The Alignment Problem In Multi Head Attention Based Neural Machine Translation
... state-of-the-art multi-head attention models based on the transformer ...the multi-head source-to-target attention compo- ...guided translation task, where the user wants to guide ... See full document
9
Attention over Heads: A Multi Hop Attention for Neural Machine Translation
... Universal Transformer (Dehghani et al., 2019) can be thought of variable-depth recurrent at- tention. It obtained Turing-complete expressive power in exchange for a vast increase in the num- ber of parameters and ... See full document
6
The Design of the SauLTC application for the English Arabic Learner Translation Corpus
... English source texts and their Arabic translations in terms of their segmentations, which in turn made automatic alignment ...by translation learners while translating multi-word units and obtaining ... See full document
9
Doubly Attentive Decoder for Multi modal Neural Machine Translation
... a multi-source attention-based NMT model trained to translate a pair of sentences in two different source languages into a target lan- guage, and reported considerable improvements over a ... See full document
12
Improving Robustness of Neural Machine Translation with Multi task Learning
... of multi-task learning for machine translation, Tu et ...the source sen- tence from the hidden layers of the translation de- ...plete source information, which helps improve the ... See full document
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