[PDF] Top 20 What do Neural Machine Translation Models Learn about Morphology?
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What do Neural Machine Translation Models Learn about Morphology?
... factored translation and reordering models (Koehn and Hoang, 2007; Durrani et ...in neural MT, although they had also been used in phrase-based MT for han- dling morphologically-rich (Luong et ...In ... See full document
12
Compression of Neural Machine Translation Models via Pruning
... Neural Machine Translation (NMT), like many other deep learning domains, typ- ically suffers from over-parameterization, resulting in large storage ... See full document
11
Incorporating Word Reordering Knowledge into Attention based Neural Machine Translation
... distortion models explicitly capture word reordering knowledge through estimating the probability distribu- tion of relative jump distances on source words to incorporate word reordering knowledge into the ... See full document
11
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
Improving Neural Machine Translation Models with Monolingual Data
... for do- main adaptation. In our analysis, we identified do- main adaptation effects, a reduction of overfitting, and improved fluency as reasons for the effective- ness of using monolingual data for ... See full document
11
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
Leveraging Rule Based Machine Translation Knowledge for Under Resourced Neural Machine Translation Models
... for translation between English, modern stan- dard Arabic and Arabic dialects, ...the translation quality, specifically if pre-trained FastText models were in- jected during the NMT training ...for ... See full document
9
Tibetan Chinese Neural Machine Translation based on Syllable Segmentation
... intelligence. Neural machine translation (NMT) is also a machine translation method that is gradually emerging at this ...of neural machine translation are as ... See full document
9
What Makes Word level Neural Machine Translation Hard: A Case Study on English German Translation
... Therefore, instead of using words as only inputs and outputs, there have been several approaches which take into account more fine-grained units such as characters. Based on the fact that rare words are often composed of ... See full document
10
Greedy Search with Probabilistic N gram Matching for Neural Machine Translation
... Neural machine translation (NMT) models are usually trained with the word-level loss using the teacher forcing algorithm, which not only evaluates the translation improperly but also ... See full document
7
What does Attention in Neural Machine Translation Pay Attention to?
... in neural machine translation provides the possibility to encode relevant parts of the source sentence at each trans- lation ...of what is being learned by attention mod- ... See full document
10
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring
... unsupervised machine translation track of the WMT’19 news shared task from German to ...tistical machine translation (PBSMT) model and a pre-trained language model to combine word-level ... See full document
8
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... proper translation has to be derived from ...the translation sys- tem is gender biased, the context is disregarded, while if the system is neutral, the translation is cor- rect (since it has the ... See full document
8
Learning to Actively Learn Neural Machine Translation
... NMT Model Our baseline model consists of a 2-layer bi-directional LSTM encoder with an em- beddings size of 512 and a hidden size of 512. The 1-layer LSTM decoder with 512 hidden units uses an attention network with 128 ... See full document
11
Coverage Embedding Models for Neural Machine Translation
... In this paper, we propose simple, yet effective, cov- erage embedding models for attention-based NMT. Our model learns a special coverage embedding vec- tor for each source word to start with, and keeps up- dating ... See full document
6
Applying Morphology Generation Models to Machine Translation
... on translation into ...the models are not integrated as tightly as possible, it offers some important ad- vantages, due to the very decoupling of the compo- ... See full document
9
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 ... See full document
8
Morphology aware Word Segmentation in Dialectal Arabic Adaptation of Neural Machine Translation
... Recurrent Neural Network (RNN) that is coupled with Con- ditional Random Fields (CRF) sequence labeler trained to segment words from four different di- alects namely Egyptian (EGY), Levantine (LEV), Gulf (GLF), ... See full document
7
Low Resource Corpus Filtering Using Multilingual Sentence Embeddings
... Zipporah models for both language pairs Sinhala–English and ...(probabilistic translation dic- tionaries and language models) were trained on the provided clean data (excluding the dictionar- ... See full document
6
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
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