[PDF] Top 20 Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation
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Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation
... for domain adaptation in neural machine transla- ...trophic forgetting of general-domain knowl- ...(EWC)—a machine learning method for learning a new task without for- ... See full document
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Incremental Domain Adaptation for Neural Machine Translation in Low Resource Settings
... for domain adaptation in the context of statistical and neural machine ...that domain, but models need to be trained from ...for domain adaptation in ...in- domain ... See full document
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Cost Weighting for Neural Machine Translation Domain Adaptation
... new domain adaptation technique for neural machine translation called cost weighting, which is appropriate for adaptation scenarios in which a small in-domain data set and ... See full document
7
Instance Weighting for Neural Machine Translation Domain Adaptation
... phrase-based machine translation domain ...to Neural Machine Translation (NMT) directly, because NMT is not a linear ...and domain weighting with a dynamic weight learning ... See full document
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Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
... a neural machine translation system (the encoder, decoder, and each embedding space) and consider each component’s contri- bution to, and capacity for, domain ...component during ... See full document
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Iterative Dual Domain Adaptation for Neural Machine Translation
... the domain adaptation for neural machine translation (NMT) mainly focus on the one-pass transferring out-of- domain translation knowledge to in-domain NMT ...the ... See full document
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Sentence Embedding for Neural Machine Translation Domain Adaptation
... for machine translation, only those that belong to the same or similar domains are typically able to improve translation ...Recently Neural Machine Translation (NMT) has become ... See full document
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Unsupervised Domain Adaptation for Neural Machine Translation with Domain Aware Feature Embeddings
... use domain tags to control the output domain, but it still needs a in-domain par- allel corpus and our architecture allows more flex- ible modifications than just adding additional ...Unsupervised ... See full document
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An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation
... (Cromieres et al., 2016). The NMT settings were the same as (Cromieres et al., 2016) except that we used a vocabulary size of 32k for all the ex- periments, and did not ensemble independently trained parameters. The ... See full document
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Domain Adaptation and Attention Based Unknown Word Replacement in Chinese to Japanese Neural Machine Translation
... The copy-based methods aim at copying relevant source words to replace unknown words. Some use existing alignment tools (Luong et al., 2015b), and others suggest that using attention scores in the ANMT models can be an ... See full document
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Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation
... 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
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Curriculum Learning for Domain Adaptation in Neural Machine Translation
... The total amount of the in-domain data in each domain is summarized in Table 3. In this paper, we uniformly sample 15k in-domain data from each dataset. We choose the amount of 15k, which makes up a ... See full document
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Domain Adaptation of Neural Machine Translation by Lexicon Induction
... in adaptation methods for ...supervised adaptation relies on in-domain paral- lel data, and unsupervised adaptation has no such ...learning domain dis- crimination and ... See full document
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Simple, Scalable Adaptation for Neural Machine Translation
... for domain adaptation in Section ...model during adaptation, but we use the adaptation stage to improve perfor- mance on languages learnt during ... See full document
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Non Parametric Adaptation for Neural Machine Translation
... to catastrophic forgetting caused by parameter shift during the training ...of Neural Ma- chine Translation (NMT) this results in poor performance on heterogeneous datasets and on ... See full document
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Improving Domain Adaptation for Machine Translation withTranslation Pieces
... Neural Machine Translation has achieved impressive results in the last couple years, in particular when aided by domain adap- tation ...on translation pieces and show that it can work ... See full document
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Evaluating Domain Adaptation for Machine Translation Across Scenarios
... of translation quality can be made by professional translators or native speak- ers, and usefulness can be assessed via measurements of productivity gains and losses when post-editing machine- translated ... See full document
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Effective Domain Mixing for Neural Machine Translation
... source domain and finetune on target-domain ...ate domain adaptation methods and propose mixing source and target domain data during ...new domain and perform well on only ... See full document
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Domain Differential Adaptation for Neural Machine Translation
... and domain sensitive, but it is nearly impos- sible to obtain large quantities of labeled data for every domain we are interested ...of domain adaptation strate- ...of Domain ... See full document
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Domain Control for Neural Machine Translation
... Machine translation systems are very sen- sitive to the domains they were trained ...Several domain adaptation techniques have already been deeply ...for neural ma- chine ... See full document
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