[PDF] Top 20 Combining Translation Memory with Neural Machine Translation
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Combining Translation Memory with Neural Machine Translation
... while the TEXT datasets does 28,507. These dif- ferent trends between the ITEM and TEXT data suggest that the systems are required to translate relatively fixed phrases or sentences more in the ITEM data set, which is a ... See full document
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Combining Statistical Machine Translation and Translation Memories with Domain Adaptation
... of translation memory software, translation companies and freelance translators have been accumulating translated text for various languages and ...domain-specific machine translation ... See full document
11
Document Context Neural Machine Translation with Memory Networks
... extend the vanilla attention-based neural MT model (Bahdanau et al., 2015) by conditioning the decoder on the previous sentence via atten- tion over its words. Extending their model to con- sider the global source ... See full document
10
The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation
... The past year has witnessed rapid ad- vances in sequence-to-sequence (seq2seq) modeling for Machine Translation (MT). The classic RNN-based approaches to MT were first out-performed by the convolu- tional ... See full document
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Hungarian translators’ perceptions of Neural Machine Translation in the European Commission
... offers translation services to other Commission Directorates-General, who send translation requests to a central DGT service that pre-processes texts automatically using various ...normative memory, ... See full document
7
Residual Stacking of RNNs for Neural Machine Translation
... of machine translation is greatly improved by applying neural networks par- tially in a Statistical Machine Translation (SMT) pipeline (Zou et ...end-to-end neural network based ... See full document
7
Guiding Neural Machine Translation with Retrieved Translation Pieces
... key-value memory to guide the NMT model for translating the real input sentence, which changes the NMT model structure and increases both the training-time and test-time computational ... See full document
11
Scaling Neural Machine Translation
... + overlap comm+bwd 128 402k 1 26.5 9.7k 1.82M 32 44.7x Table 1 : Training time (min) for reduced precision ( 16-bit ), cumulating gradients over multiple back- wards (cumul), increasing learning rate (2x lr) and ... See full document
9
Variational Neural Machine Translation
... Following the success of attentional NMT, a num- ber of approaches and models have been proposed for NMT recently, which can be grouped into differ- ent categories according to their motivations: deal- ing with rare ... See full document
10
Tensor2Tensor for Neural Machine Translation
... While RNNs represent sequence history in their hidden state, the Transformer has no such fixed-size bottleneck. Instead, each timestep has full direct access to the history through the dot-product attention mechanism. ... See full document
7
Multi Module Recurrent Neural Networks with Transfer Learning
... from neural network ma- chine translation ...on combining all three sys- tems: (1) Neural CRF (Conditional Random Fields), trained directly on the metaphor data set; (2) Neural ... See full document
5
Memory Augmented Neural Networks for Machine Translation
... machine translation. Two of these models; NTM Style Attention and the Memory-Augmented Decoder extend the atten- tional encoder-decoder which has achieved state- of-the-art results on many language ... See full document
10
Memory enhanced Decoder for Neural Machine Translation
... There is a long thread of work aiming to im- prove the ability of RNN in remembering long se- quences, with the long short-term memory RNN (LSTM) (Hochreiter and Schmidhuber, 1997) be- ing the most salient ... See full document
9
Memory augmented Neural Machine Translation
... of neural models: the trans- lation function, represented by various neural net- works, is shared amongst all of the translation pairs, so high-frequency and low-frequency pairs impact each other by ... See full document
10
Graph Based Translation Memory for Neural Machine Translation
... the translation results of P-TFM and G- TFM ...lar translation memory is ...the translation task where the TM is very similar to test ... See full document
8
Incorporating Discrete Translation Lexicons into Neural Machine Translation
... In this paper, we propose a simple, yet effective method to incorporate discrete, probabilistic lexi- cons as an additional information source in NMT (§3). First we demonstrate how to transform lexi- cal ... See full document
11
Boosting Neural Machine Translation
... Training efficiency is one of the main problems for Neural Machine Translation (NMT). Deep networks need for very large data as well as many training iterations to achieve state-of-the-art ... See full document
6
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
... This talk describes our recent work on developing unsupervised speech technology, where transcripts and pronunciation dictionaries are not used. The work is inspired by considering both how young infants may begin to ... See full document
74
A machine translation system combining rule based machine translation and statistical post editing
... baseline translation is perfectly equal to the reference ...EIWA’s translation is more literal than baseline ...additional translation or scrambling of reference (Isozaki et ... See full document
5
UCSYNLP Lab Machine Translation Systems for WAT 2019
... years, Neural Machine Translation (NMT) (Bahdanau et ...Statistical Machine Translation (SMT) ...the machine translation based on neural ... See full document
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