[PDF] Top 20 Neural Machine Translation with Source Dependency Representation
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Neural Machine Translation with Source Dependency Representation
... Source dependency information has been successfully introduced into statistical machine ...for Neural Machine Translation (NMT), such as concatenating representations of ... See full document
7
Exploiting Source side Monolingual Data in Neural Machine Translation
... of source-side monolingual data in con- ventional SMT with a self-learning ...more translation rules in SMT and we also adapt a multi-task learning framework to take full advantage of the source-side ... See full document
11
Evaluating Layers of Representation in Neural Machine Translation on Part of Speech and Semantic Tagging Tasks
... While neural machine translation (NMT) models provide improved translation qual- ity in an elegant framework, it is less clear what they learn about ...on source-side representa- tions, ... See full document
10
Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
... those containing only common words (Sutskever et al., 2014; Bahdanau et al., 2015). The rare word problem is exacerbated when translating from morphologically rich languages, where the several morphological variants of ... See full document
6
Neural Machine Translation with Source Side Latent Graph Parsing
... novel neural ma- chine translation model which jointly learns translation and source-side latent graph representations of ...ral machine translation model, and thus the parser is ... See full document
11
Encoding Source Language with Convolutional Neural Network for Machine Translation
... The Role of Guiding Signal It is slight sur- prising that the generic CNN can also achieve the gain on BLEU similar to that of BBN- JM, since intuitively generic CNN encodes the entire sentence and the representations ... See full document
11
Unsupervised Source Hierarchies for Low Resource Neural Machine Translation
... For translations out of English, we also consider an upper bound that uses syntactic supervision; we dub this model parse2seq. This is based on the mixed RNN model proposed by Li et al. (2017). We parse the source ... See full document
7
Multi Source Transformer for Kazakh Russian English Neural Machine Translation
... using SRILM (Stolcke, 2002), and a pruned 6- gram LM trained on the monolingual training cor- pora (for en2ru, trained just on news using KenLM (Heafield, 2011); for ru2kk and en2kk, a static mixture LM trained on all ... See full document
8
Document Level Information as Side Constraints for Improved Neural Patent Translation
... and neural translation models for patent ...phrase-based machine translation, our features based on annotation overlap between test documents and phrase context were not ...For neural ... See full document
12
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings
... 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
10
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... Neural machine translation has significantly pushed forward the quality of the ...ness. Neural models are trained on large text corpora which contain biases and ...in neural ma- chine ... See full document
8
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
Ensembling Factored Neural Machine Translation Models for Automatic Post Editing and Quality Estimation
... original translation as a black box, which cannot be modified or ...a source sentence and a translation ...multi-source machine translation (Zoph and Knight, 2016; Firat et ... See full document
8
Distilling Knowledge for Search based Structured Prediction
... In Section 4.2, improvements from distilling the ensemble have been witnessed in both the transition-based dependency parsing and neural machine translation experiments. However, ques- tions ... See full document
10
Target Foresight Based Attention for Neural Machine Translation
... based neural network for image caption task and ad- vance the state-of-the-art results; Yin et ...of machine translation, the idea of attention based neu- ral networks has been pioneered by Bahdanau ... See full document
11
Multi Source Neural Machine Translation with Missing Data
... We describe the settings of common parts for all NMT models: multi-encoder NMT, mixture of NMT experts, and one-to-one NMT. We used global attention and attention feeding (Luong et al., 2015) for the NMT models and used ... See full document
8
OpenNMT: Open Source Toolkit for Neural Machine Translation
... As a case study, we adapted two systems with non-textual inputs to run in OpenNMT. The first is an image-to-text system developed for mathe- matical OCR (Deng et al., 2016). This model re- places the source RNN ... See full document
6
OpenNMT: Neural Machine Translation Toolkit
... Neural machine translation (NMT) is a new methodology for machine translation that has led to remarkable improvements, particularly in terms of human evaluation, compared to rule-based ... See full document
8
Incorporating Source Syntax into Transformer Based Neural Machine Translation
... parsing results. Overall, Romance, Germanic, and Hellenic target language systems generate the fewest valid parses. This indicates that Baltic, Slavic, and Uralic target languages are most help- ful in learning to parse ... See full document
10
Sequence to Dependency Neural Machine Translation
... generated translation and its corresponding dependency ...the translation of SMT is disfluent and ungram- matical, whereas RNNsearch is better than ...the translation of RNNsearch is locally ... See full document
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