[PDF] Top 20 Adapting Neural Machine Translation with Parallel Synthetic Data
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Adapting Neural Machine Translation with Parallel Synthetic Data
... Josep Maria Crego, Jungi Kim, Guillaume Klein, An- abel Rebollo, Kathy Yang, Jean Senellart, Egor Akhanov, Patrice Brunelle, Aurelien Coquard, Yongchao Deng, Satoshi Enoue, Chiyo Geiss, Joshua Johanson, Ardas Khalsa, ... See full document
10
Neural Text Simplification in Low Resource Conditions Using Weak Supervision
... a data-demanding paradigm have dealt with the English language, for which sizeable training datasets are currently available to de- ploy competitive ...training data, or to the design of ef- fective ... See full document
8
On the Impact of Various Types of Noise on Neural Machine Translation
... clean parallel corpus and potentially noisy data to it, this work can be seen as a type of data ...ditional parallel corpora by copying monolingual corpora in the target language into the ... See full document
10
Improving Back Translation with Uncertainty based Confidence Estimation
... low-resource neural machine translation (NMT), the synthetic bilingual cor- pora generated by NMT models trained on limited authentic bilingual data are inevitably ...in ... See full document
12
Hybrid Data Model Parallel Training for Sequence to Sequence Recurrent Neural Network Machine Translation
... patent translation involving neural networks. Data parallel- ism and model parallelism are two com- mon approaches for reducing training time using multiple graphics processing units (GPUs) on ... See full document
9
Handling Rare Word Problem using Synthetic Training Data for Sinhala and Tamil Neural Machine Translation
... Neural Machine Translation (NMT) is the current state-of- the-art machine translation architecture that aims at building a single neural network that can be jointly tuned to ... See full document
5
Improving Statistical Machine Translation by Adapting Translation Models to Translationese
... of translation in the context of SMT. They found that a translation model based on the S → T portion of the parallel corpus results in much better translation quality than a translation ... See full document
26
Japanese Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019
... Asian Translation (Nakazawa et ...of parallel data is only 12k parallel sentences; (b) how distant given language pair is, in terms of different writing system, phonology, morphology, grammar, ... See full document
6
Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models
... LHUC introduces an additional multiplicative amplitude element to the output of each hidden unit in the network. As such the contribution of the hidden unit can be amplified (values greater than 1) for the units that are ... See full document
6
Data Augmentation for Low Resource Neural Machine Translation
... Given a source and target sentence pair (S,T), we want to alter it in a way that preserves the semantic equivalence between S and T while diversifying as much as possible the training examples. A number of ways to do ... See full document
7
Soft Contextual Data Augmentation for Neural Machine Translation
... four translation tasks are presented in Table ...the translation tasks and 2) unlike other methods that may not be powerful in all tasks, our method universally works well regardless of the ...of ... See full document
6
Exploiting Out of Domain Parallel Data through Multilingual Transfer Learning for Low Resource Neural Machine Translation
... (b) parallel sentence mining from non-parallel or comparable corpora (Utiyama and Isahara, 2003; Tillmann and Xu, 2009), (c) generating pseudo-parallel data (Sennrich et ...does ... See full document
12
Improving Low Resource Neural Machine Translation with Filtered Pseudo Parallel Corpus
... the translation model (Wang et ...high-quality parallel sentence pairs and achieve better translation performance and reduce time- complexity with a small high-quality ...filters data by ... See full document
9
Exploiting Monolingual Data at Scale for Neural Machine Translation
... back translation (BT) approach to leverage the target-side monolingual data, which is simple and ...monolingual data. The translation output and the target-side monolingual data then ... See full document
10
Improving Neural Machine Translation Models with Monolingual Data
... target data into the source language, and treating this synthetic data as additional train- ing ...monolingual data, back-translated into the source language, can be effectively used for do- ... See full document
11
Iterative Back Translation for Neural Machine Translation
... monolingual data is a similar domain to the test ...Farsi translation is much more difficult than French; or a result of the diverse mix of domains in the parallel training data (news with LDC ... See full document
7
Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data
... monolingual data available for each ...the parallel corpora . Tables 1 to 4 show the parallel datasets used for training for each translation di- rection after ...the data for EN–KK’s ... See full document
7
IITP MT System for Gujarati English News Translation Task at WMT 2019
... of parallel data is available, we explore the back- translation technique for this ...for machine translation for many lan- guage ...create synthetic parallel data ... See full document
5
Building English to Serbian Machine Translation System for IMDb Movie Reviews
... a machine translation system for user- generated content involving a complex South Slavic ...on translation of English IMD b user movie reviews into Ser- bian, in a low-resource ...and neural ... See full document
9
Improving Neural Machine Translation Using Noisy Parallel Data through Distillation
... the machine learning literature, various meth- ods have been proposed for efficient learning with label ...unlabeled data can be obtained by predictions of another ...noisy data cause lower ... See full document
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