[PDF] Top 20 Simple, Scalable Adaptation for Neural Machine Translation
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Simple, Scalable Adaptation for Neural Machine Translation
... While several approaches have been explored in literature (Chu and Wang, 2018), full fine- tuning of model parameters remains the dominant approach for adapting to new domains and lan- guages (Luong and Manning, 2015; ... See full document
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Simple and Effective Noisy Channel Modeling for Neural Machine Translation
... very simple and effective ...Our simple noisy channel approach consistently outperforms strong base- lines such as online ensembles and left-to-right re- ranking setups ... See full document
6
A Simple and Effective Approach to Coverage Aware Neural Machine Translation
... Another interesting question is whether the N- MT systems can generate translations with ap- propriate lengths. To seek its answer, we stud- ied the length difference between the MT output and the shortest reference. ... See full document
6
Curriculum Learning for Domain Adaptation in Neural Machine Translation
... While simple to motivate, this may not always perform well be- cause neural methods benefit from randomization in the minibatches and multiple ...for neural net- work optimization can be ... See full document
13
Tutorial: De mystifying Neural MT
... Neural Statistical Machine Translation Neural Machine Translation Encoder Decoder Sequence-to-sequence learning: Encoder Sequence-to-sequence learning: Decoder Let’s use a simple NN for [r] ... See full document
84
Extreme Adaptation for Personalized Neural Machine Translation
... chine Translation (MT), these variations have a significant effect on how the sys- tem should perform translation, but this is not captured well by standard one-size- fits-all ...a simple and ... See full document
7
Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation
... speaker adaptation. Their work adapts a context dependent deep neural network hidden Markov model (CD-DNN-HMM) using the KL-divergence between the softmax out- puts (modeling tied-triphone states) of a ... See full document
9
Improving Back Translation with Uncertainty based Confidence Estimation
... is simple and effec- tive in exploiting abundant monolingual cor- pora to improve low-resource neural machine translation (NMT), the synthetic bilingual cor- pora generated by NMT models ... See full document
12
Non Parametric Adaptation for Neural Machine Translation
... To retrieve useful candidates when sentence similarity is low, we use n-gram retrieval instead of sentence retrieval. This results in neighbors which have high local overlap with the source sen- tence, even if they are ... See full document
11
Sentence Level Adaptation for Low Resource Neural Machine Translation
... domain adaptation where a general NMT sys- tem is trained on a large amounts of out-of-domain parallel data; then, the general model is adapted for a particular ...document-level adaptation of Kothur et ... See full document
9
Freezing Subnetworks to Analyze Domain Adaptation in Neural Machine Translation
... In all cases, freezing the target embeddings has very little impact (at most − 0.2 BLEU, in Ko–En), suggesting that it is relatively unimportant during adaptation. These results show that the model and training ... See full document
9
Simple and Effective Parameter Tuning for Domain Adaptation of Statistical Machine Translation
... Statistical Machine Translation systems are based on log-linear models that combine a set of feature functions to score translation hypotheses during ... See full document
16
Multi Domain Neural Machine Translation through Unsupervised Adaptation
... recurrent neural network decodes the source hidden sequence into the target ...a translation requires sam- pling at each step the most probable target word from the distribution and then feeding it back to ... See full document
11
Incremental Domain Adaptation for Neural Machine Translation in Low Resource Settings
... on translation time and quality of incremental model ...post-edit translation candidates, translations that improve over time might reduce this post- editing effort and, consequently reduce the over- all ... See full document
10
Domain Adaptation and Attention Based Unknown Word Replacement in Chinese to Japanese Neural Machine Translation
... Chinese-to-Japanese translation task of scientific ...domain adaptation system, and the ensemble of the two ...domain adaptation method boosts the BLEU score by ... 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
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
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
Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation
... Ondˇrej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Shujian Huang, Matthias Huck, Philipp Koehn, Qun Liu, Varvara Logacheva, Christof Monz, Matteo Negri, Matt Post, Raphael Rubino, Lucia ... See full document
7
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
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