[PDF] Top 20 Coverage Embedding Models for Neural Machine Translation
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Coverage Embedding Models for Neural Machine Translation
... For all NMT systems, the full vocabulary sizes for thr two training sets are 300k and 500k respectively. The coverage embedding vector size is 100. In the training procedure, we use AdaDelta (Zeiler, 2012) ... See full document
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Leveraging Rule Based Machine Translation Knowledge for Under Resourced Neural Machine Translation Models
... Rule-based machine translation is a ma- chine translation paradigm where linguistic knowledge is encoded by an expert in the form of rules that translate from source to target ...a machine ... See full document
9
On Using Very Large Target Vocabulary for Neural Machine Translation
... four models from each of which two points corresponding to the best and second- best performance on the development set were col- ...eight models from which we averaged the length-normalized ... See full document
10
Multimodal Machine Translation with Embedding Prediction
... the models us- ing the F-score of each word; this shows how accu- rately each word is translated into target sentences, as was proposed in Kumar and Tsvetkov ... See full document
6
Tibetan Chinese Neural Machine Translation based on Syllable Segmentation
... of neural machine translation, phrase- based statistical machine translation model Nitutrans (Xiao T et ...cal machine translation model. In this paper, four models ... See full document
9
Greedy Search with Probabilistic N gram Matching for Neural Machine Translation
... We apply our method to an attention-based NMT system (Bahdanau et al., 2014) implemented by Pytorch. Both source and target vocabularies are limited to 30K. All word embedding sizes are set to 512, and the sizes ... See full document
7
Incorporating Word Reordering Knowledge into Attention based Neural Machine Translation
... Parameters are updated by Mini-batch Gra- dient Descent and the learning rate is con- trolled by the AdaDelta (Zeiler, 2012) algorith- m with decay constant ρ = 0.95 and denomi- nator constant ϵ = 1e − 6. The batch size ... See full document
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Semi Supervised Neural Machine Translation with Language Models
... We start from evaluating four main models on En–Fr-20k and En-Ru-20k datasets. The training progress for En–Fr pair is shown on Fig. 2. The final results for both pairs on a test set are listed in table 2. We can ... See full document
8
Beyond Weight Tying: Learning Joint Input Output Embeddings for Neural Machine Translation
... Tying the weights of the target word em- beddings with the target word classifiers of neural machine translation models leads to faster training and often to better translation quality. ... See full document
11
The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2017
... attention neural machine translation model can be benefit by constraining the attentional process to adequately cover the source words (Sankaran et ...attentional neural net- work. Our ... See full document
8
Recurrent Stacking of Layers for Compact Neural Machine Translation Models
... Third, when we used deeper RS than what had been used for training, the BLEU score started dropping again. This in- dicates that the model has not learned to extract complex fea- tures beyond what it has been trained ... See full document
8
Knowledge Based Semantic Embedding for Machine Translation
... As shown in Table 2, given the source sentence, Source Grounding part tries to extract the seman- tic information, and map it to the tuples of knowl- edge base. It is worth noticing that the tuples are ... See full document
10
Converting Continuous Space Language Models into N Gram Language Models for Statistical Machine Translation
... Another approach is using restricted Boltzmann machines (RBMs) (Niehues and Waibel, 2012) instead of using multi-layer neural networks (Bengio et al., 2003; Schwenk, 2007; Le et al., 2011). Since probability in a ... See full document
6
Towards one shot learning for rare word translation with external experts
... in neural machine translation is a rich and active topic, particularly when translating morphologically rich languages or translating named ...most neural trans- lation systems (Wu et ...the ... See full document
10
Low Resource Corpus Filtering Using Multilingual Sentence Embeddings
... tistical machine translation (Moses, phrase-based (Koehn et ...the neural machine trans- lation system fairseq (Ott et ...the machine transla- tion system will be measured by BLEU score ... See full document
6
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... this translation contains gender bias since it ig- nores the fact that, for both cases, “friend” is a female and translates by focusing on the occupa- tional stereotypes, ... See full document
8
Compression of Neural Machine Translation Models via Pruning
... language models), the model size of NMT is still prohibitively large for mobile ...deeper neural networks has brought great progress, it has also introduced over-parameterization, resulting in long running ... See full document
11
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
7
Robust Neural Machine Translation with Joint Textual and Phonetic Embedding
... β = 0.4, 0.6, 0.8, 0.95, the BLEU scores improves 2 − 3 points. Second, the phonetic information plays a very important role in translation. Even when β = 0.95, that is, the weight of phonetic embedding is ... See full document
6
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation
... learned translation equivalences between word pairs from two monolingual ...the embedding of the vocabulary for the encoder and decoder of ...naive translation knowledge to enable the ... See full document
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