[PDF] Top 20 Effective Adversarial Regularization for Neural Machine Translation
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Effective Adversarial Regularization for Neural Machine Translation
... A regularization technique based on adversar- ial perturbation, which was initially developed in the field of image processing, has been suc- cessfully applied to text classification tasks and has yielded ... See full document
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Pre editing Plus Neural Machine Translation for Subtitling: Effective Pre editing Rules for Subtitling of TED Talks
... The translation quality of volunteer-created subtitles is regarded to be close to the professional quality because TED volunteer translators have to go through a rigid translation process involving multiple ... See full document
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A Simple and Effective Approach to Coverage Aware Neural Machine Translation
... Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan ... See full document
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Effective Approaches to Attention based Neural Machine Translation
... We compare our NMT systems in the English- German task with various other systems. These include the winning system in WMT’14 (Buck et al., 2014), a phrase-based system whose language models were trained on a huge ... See full document
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Simple and Effective Noisy Channel Modeling for Neural Machine Translation
... on neural noisy channel mod- eling relied on latent variable models that in- crementally process the source and target sen- ...with neural language models trained on bil- lions of words show that noisy ... See full document
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ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems
... of machine translation (MT) ...ral machine translation (NMT): based on encoder- decoder architectures (also known as seq2seq), NMT can use recurrent neural networks (RNNs) (Sutskever et ... See full document
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Marian: Cost effective High Quality Neural Machine Translation in C++
... batched translation. Adding a shortlist im- proves translation speed ...single-sentence translation and similar performance to MKL for batched ...single-sentence translation achieves the same ... See full document
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Scaling Neural Machine Translation
... As an alternative, we construct sub-batches so that each one takes approximately the same amount of processing time across all workers. We first set a target for the amount of time a sub-batch should take to process ... See full document
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Variational Neural Machine Translation
... Kingma et al. (2014) revisit the approach to 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 ... See full document
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Machine Translation Evaluation using Recurrent Neural Networks
... The metric uses Glove word vectors (Penning- ton et al., 2014) and the simple LSTM, the de- pendency Tree-LSTM and neural network imple- mentations by Tai et al. (2015). Training is per- formed using a mini batch ... See full document
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Generalizing Back Translation in Neural Machine Translation
... We analyze the performance of our techniques on a closed- and open-domain of the WMT 2018 German ↔ English news translation task. We provide qualitative and quantitative evidence of the detrimental behaviours and ... See full document
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Non-Autoregressive Machine Translation with Auxiliary Regularization
... sampled translation from the teacher model, out from the source side sentences, as the bilin- gual training ...a neural network is less noisy and more ... See full document
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Bidirectional Generative Adversarial Networks for Neural Machine Translation
... and columns in the structure (Glorot and Ben- gio, 2010). Then we pre-train NMT and lan- guage models with MLE principle to convergence, and select the best model according to the per- formances on the validation set, ... See full document
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Regularization techniques for fine tuning in neural machine translation
... Neural machine translation (Bahdanau et ...accuracy, neural machine translation, like most other large machine learning systems, requires large amounts of training ... See full document
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Unsupervised Neural Machine Translation with SMT as Posterior Regularization
... (1) Our proposed method significantly improves the per- formance over the “NMT” and “PBSMT” of (Lample et al. 2018). This is because unsupervised NMT methods suffer from the noise problem while PBSMT is inherently defi- ... See full document
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Robust Neural Machine Translation with Doubly Adversarial Inputs
... Table 8 shows the importance of different com- ponents in our approach, which include L clean , L robust and L lm . As for L robust , it includes the source adversarial input, i.e. x 0 6= x and the target source ... See full document
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Japanese Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019
... is effective for multilingual neural machine translation model in extremely low resource ...the translation quality of Japanese↔Russian language pair, our method leverages other ... See full document
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Effective Domain Mixing for Neural Machine Translation
... There is some overlap between past research in multi-domain statistical machine transla- tion (SMT) and the ideas of this paper. (Fara- jian et al., 2017) compared the efficacy of phrase-based SMT and NMT on ... See full document
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Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
... because neural networks usually impose strong indepen- dence assumptions between hidden ...a neural model requires that the interdependence of information sources be mod- eled explicitly (Tu et ... See full document
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Improving Neural Machine Translation with Conditional Sequence Generative Adversarial Nets
... Chinese-English translation task. Table 3 presents the translation performance of the BR- CSGAN on the test sets when the N are set from 5 to 30 with interval ...the translation performance of the ... See full document
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