[PDF] Top 20 Multi Domain Neural Machine Translation through Unsupervised Adaptation
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Multi Domain Neural Machine Translation through Unsupervised Adaptation
... out test corpus. Duplicated sentence pairs are re- moved from each corpus separately, resulting in a total of 3,527 dev and 6,962 test corpora for all the domains. To analyze the performance of the sys- tem on generic ... See full document
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Incremental Domain Adaptation for Neural Machine Translation in Low Resource Settings
... reduces translation cost and time by ...(i) Domain mismatch: the genre of samples of MEDAR dataset is signif- icantly different from the domain of the samples observed in M gen (Almahairi et ... See full document
10
Regularized Training Objective for Continued Training for Domain Adaptation in 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
9
Unsupervised Multi Domain Adaptation with Feature Embeddings
... with neural word embeddings from Collobert and Weston (2008) and Mnih and Hinton ...each domain, following EasyAdapt (Daum´e III, ...to unsupervised domain adapta- tion, and do not work in the ... See full document
11
Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation
... for domain adaptation in neural machine transla- ...(EWC)—a machine learning method for learning a new task without for- getting previous ...in- domain performance, outperforming ... See full document
7
Simplified Neural Unsupervised Domain Adaptation
... Recently, neural-network-based domain adap- tation algorithms have been successful, including domain adversarial methods (Ganin et ...a neural ver- sion of SCL still obtains near ... See full document
6
Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
... As a final preprocessing step, we train Byte Pair Encoding (BPE) segmentation models (Sennrich et al., 2016) on the out-of-domain training corpus. We train separate BPE models for each language, each with a ... See full document
9
An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation
... of neural ma- chine translation (NMT) (Bahdanau et ...alignments, translation rules and complicated decoding algorithms, which are a characteristic of statistical machine transla- tion (SMT) ... See full document
7
Unsupervised Neural Machine Translation with Weight Sharing
... a multi-head self-attention and a simple position-wise fully connected feed-forward ...performs multi-head attention over the output of the encoder ...the multi-head self-attention layer, we refer ... See full document
10
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
9
Simple, Scalable Adaptation for Neural Machine Translation
... In this work we propose using light-weight adapter layers, which are transplanted between the layers of a pre-trained network and fine-tuned on the adaptation corpus. Adapting only the light- weight layers enables ... See full document
11
Document Level Adaptation for Neural Machine Translation
... adapt machine translation systems to novel domains, but even a well-adapted system may be able to perform better on a particular document if it were to learn from a translator’s cor- rections within the ... See full document
10
Extreme Adaptation for Personalized Neural Machine Translation
... Domain adaptation techniques for MT often rely on data selection (Moore and Lewis, 2010; Li et ...adding domain tags to NMT input (Chu et ...baseline adaptation strategy of tuning all ... See full document
7
Domain Control for Neural Machine Translation
... about domain to the network. However, we introduce domain information at the sentence ...a domain adapted model in a very limited training ...performing domain-adapted translations using a ... See full document
7
Neural vs Phrase Based Machine Translation in a Multi Domain Scenario
... Neural machine translation systems have recently outperformed their conventional statistical coun- terparts in the translation tasks in several domains such as news (Sennrich et ...target ... See full document
5
Phrase Based & Neural Unsupervised Machine Translation
... word-by-word translation with an inferred bilingual ...each domain to infer the structure in the data (underlying continuous curve); it acts as a data-driven prior to denoise/correct sentences (illustrated ... See full document
11
Unsupervised Neural Machine Translation with Future Rewarding
... Recently, motivated by the success of cross- lingual embeddings (Artetxe et al., 2016; Zhang et al., 2017; Conneau et al., 2017), several works have tried to train NMT or SMT models using un- supervised setting, in which ... See full document
10
Cost Weighting for Neural Machine Translation Domain Adaptation
... Starting from random parameters for both mod- els, we alternate between optimizing the weighted NMT objective in Equation 7, and the classifier’s cross-entropy objective. Training the two concur- rently allows the ... See full document
7
Neural Lattice Search for Domain Adaptation in Machine Translation
... Ondˇrej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Varvara Logacheva, Christof Monz, Matteo Negri, Aure- lie Neveol, Mariana Neves, ... See full document
6
Sentence Embedding for Neural Machine Translation Domain Adaptation
... Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Constantin, and Evan ... See full document
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