[PDF] Top 20 Non Parametric Adaptation for Neural Machine Translation
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Non Parametric Adaptation for Neural Machine Translation
... overall translation quality NMT has shown some glaring weaknesses, including idiom processing, and rare word or phrase translation (Koehn and Knowles, 2017; Isabelle et ...tional neural network ... See full document
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Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation
... at translation time, which may be impractical for some appli- cations and does not address our more fundamen- tal goal of building a single model that is robust across ... See full document
7
Incremental Domain Adaptation for Neural Machine Translation in Low Resource Settings
... In this work, we implement a new query strat- egy for selecting “unlabeled” instances from a tar- get domain and investigate its effect on fine-tuning a generic NMT model. We borrow techniques and terms from the active ... See full document
10
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
8
Imitation Learning for Non Autoregressive Neural Machine Translation
... is thus significantly boosted. However, it comes at the cost that the translation quality is largely sacrificed since the intrinsic dependency within the natural language sentence is abandoned. A bulk of work has ... See full document
9
Multi Domain Neural Machine Translation through Unsupervised Adaptation
... proposed an instance-based adaptation technique for NMT in which for each translation segment a set of similar sentence pairs is retrieved. This small training set is then used to update the model before ... See full document
11
Sentence Level Adaptation for Low Resource Neural Machine Translation
... Li et al. (2016) present a dynamic NMT ap- proach where the general NMT model is adapted per-sentence; however, they adapt on only a single similar sentence and employ their system in a high- resource context. We propose ... See full document
9
Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
... Ondˇrej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Shujian Huang, Matthias Huck, Philipp Koehn, Qun Liu, Varvara Lo- gacheva, Christof Monz, Matteo Negri, Matt Post, Raphael Rubino, Lucia ... See full document
9
A Markov Model of Machine Translation using Non parametric Bayesian Inference
... PYP Translation Model We draw the distributions for the various transla- tion factors from respective hierarchical PYP pri- ors, as shown in Figure 2 for the finish, jump and emission ... See full document
10
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
... land machine translation systems submit- ted to the WMT17 German-English Ban- dit Learning ...a translation system to a new domain, using only bandit feedback: the system receives a German sentence ... See full document
7
Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation
... chine Translation (SMT) systems is the dearth of high-quality bitext in the domain of ...is adaptation data selection: the idea is to use language models (LMs) trained on in-domain text to select similar ... See full document
6
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
9
Morphology aware Word Segmentation in Dialectal Arabic Adaptation of Neural Machine Translation
... public neural machine translation framework which sup- ports sentence piece tokenization with its two vari- ant BPE and SR (unigram language model) as well as word tokenization which is basically ... See full document
7
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
Unsupervised Domain Adaptation for Neural Machine Translation with Domain Aware Feature Embeddings
... While neural machine translation (NMT) sys- tems have proven to be effective in scenarios where large amounts of in-domain data are avail- able (Gehring et ... See full document
6
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... Bolukbasi et al. (2016) uses a set of words to define the gender direction and to neutralize and equalize the bias from the word vectors. Three set of words are used: One set of ten pairs of words such as woman-man, ... See full document
8
Domain Differential Adaptation for Neural Machine Translation
... The main intuition behind our method is that models with different data requirements, namely LMs and NMT models, exhibit similar behavior when trained on the same domain, but there is little correlation between models ... See full document
11
Domain Adaptation of Neural Machine Translation by Lexicon Induction
... that neural ma- chine translation (NMT) is very sensitive to domain ...unsupervised adaptation method which fine- tunes a pre-trained out-of-domain NMT model using a pseudo-in-domain ...pairwise ... See full document
13
An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation
... In this paper, we proposed a novel domain adapta- tion method named “mixed fine tuning” for NMT. We empirically compared our proposed method against fine tuning and multi domain methods, and have shown that it is ... See full document
7
Cost Weighting for Neural Machine Translation Domain Adaptation
... With self-training (Ueffing and Ney, 2007; Schwenk, 2008; Bertoldi and Federico, 2009), an MT system trained on general domain data is used to translate large in-domain monolingual data. The resulting bilingual sentence ... See full document
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