[PDF] Top 20 Semi Supervised Neural Machine Translation with Language Models
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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
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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 ...use language models (LMs) trained on in-domain text to select similar sentences from large general-domain ... See full document
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Semi Supervised Neural Text Generation by Joint Learning of Natural Language Generation and Natural Language Understanding Models
... gagement was unknown. Hence, the Wikipedia dataset seems an ecological target for research in NLG. However, as pointed out by the authors, the Wikipedia dataset is not ideal for machine learn- ing. First, the data ... See full document
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Semi supervised Word Sense Disambiguation with Neural Models
... by averaging context vectors of all training sentences of the same sense. We observed in a few cases that the context vector is far from the held-out word’s embedding, especially when the input sentence is not ... See full document
12
Semi supervised sequence tagging with bidirectional language models
... of neural network archi- tectures for NLP ...general semi-supervised approach for adding pre- trained context embeddings from bidi- rectional language models to NLP sys- tems and apply ... See full document
10
NICT’s Supervised Neural Machine Translation Systems for the WMT19 News Translation Task
... We used Marian toolkit (Junczys-Dowmunt et al., 2018) 13 to build competitive NMT systems based on the Transformer (Vaswani et al., 2017) archi- tecture. We used the byte pair encoding (BPE) algorithm (Sennrich et al., ... See full document
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Syntactically Supervised Transformers for Faster Neural Machine Translation
... Implementation details: To ensure that the num- ber of input and output tokens in the second de- coder are equal, which is a requirement of the Transformer decoder, we add placeholder <MASK> tokens to the chunk ... See full document
13
Cross Language Text Classification by Model Translation and Semi Supervised Learning
... on machine translation to generate parallel texts, fol- lowed by a cross-lingual projection of subjectivity labels, which are used to train subjectivity annota- tion tools for Romanian and ...cross- ... See full document
11
Supervised neural machine translation based on data augmentation and improved training & inference process
... For this subtask, we utilized only the data in ASPEC, no data augmentation was used. We implemented the system based on OpenNMT 1.22.0, and adapted the beam search bug fix in the afterwards versions. We hired ... See full document
5
Coverage Embedding Models for Neural Machine Translation
... gram language models are trained on the English side of the parallel corpus, and on monolingual corpora (around 10 billion words from Gigaword (LDC2011T07)), ... See full document
6
Neural Network Language Models for Candidate Scoring in Hybrid Multi System Machine Translation
... detailed translation experiments where a BLEU score was obtained in every stage of the LM training there was only a steady correlation of BLEU and perplexity in the case of using Hugo and Yandex translations, ... See full document
8
Pragmatic Neural Language Modelling in Machine Translation
... integrating neural language models in translation ...Scaling neural lan- guage models is a difficult task, but crucial for real-world ...ral models is necessary and what ... See full document
10
Multilingual Neural Machine Translation with Language Clustering
... on language family is consistent with the human knowledge, easy to understand, and does not change with respect to ...on language family does not cover all the languages in the world since some languages ... See full document
11
Bayesian Semi Supervised Chinese Word Segmentation for Statistical Machine Translation
... clude translation models in two directions, a lan- guage model, a distortion model and a sentence length ...The language model is a statistical ngram model estimated using modi- fied Kneser-Ney ... See full document
8
Neural Machine Translation into Language Varieties
... our models, with a 512 embedding di- mension and hidden units, and 6 layers of self- attention encoder-decoder ...all models are ob- served to converge within these ...All models are trained using ... See full document
9
Self Supervised Neural Machine Translation
... strong language models and back-translation, and build complex architectures that combine denoising au- toencoders, back-translation steps and shared en- coders among ... See full document
7
Semi Supervised Learning for Neural Machine Translation
... In this paper, we propose semi-supervised learning for neural machine translation. Given la- beled (i.e., parallel corpora) and unlabeled (i.e., monolingual corpora) data, our approach ... See full document
10
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring
... German-Czech language pair are built based on the previously proposed unsupervised MT sys- tems, with some adaptations made to accom- modate the morphologically rich characteristics of German and Czech (Tsarfaty ... See full document
8
Deep Neural Language Models for Machine Translation
... It is worth mentioning another active line of re- search in building end-to-end neural MT systems (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014; Bahdanau et al., 2015; Luong et al., 2015; Jean et al., ... See full document
5
Neural Machine Translation with Supervised Attention
... for neural machine ...attention models that were learned in an unsupervised ...a supervised manner, therefore our approach is orthogonal to ... See full document
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