[PDF] Top 20 Deep Neural Language Models for Machine Translation
Has 10000 "Deep Neural Language Models for Machine Translation" found on our website. Below are the top 20 most common "Deep Neural Language Models for Machine Translation".
Deep Neural Language Models for Machine Translation
... has been an active body of work recently in uti- lizing neural language models (NLMs) to improve translation quality. However, to the best of our knowledge, work in this direction only makes ... 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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Learning Deep Transformer Models for Machine Translation
... chine translation (He et ...to machine transla- tion, deep Transformer encoders are also used for language modeling (Devlin et ...character language model with a 64- layer Transformer ... See full document
13
Adaptive Language and Translation Models for Interactive Machine Translation
... interactive machine translation (IMT) system (Foster et ...a translation model (TM) and a language model (LM) used jointly to produce pro- posals that are appropriate translations of source ... See full document
8
Multilingual Neural Machine Translation with Language Clustering
... Multilingual neural machine translation (NMT), which translates multiple languages using a single model, is of great practi- cal importance due to its advantages in simplifying the training process, ... See full document
11
Large Language Models in Machine Translation
... The process is illustrated in Figure 2 assuming a trigram model and a decoder policy of pruning to the four most promising hypotheses. The four ac- tive hypotheses (indicated by black disks) at time t are: There is, ... See full document
10
Deep Neural Network Language Models
... years, neural network language mod- els (NNLMs) have shown success in both peplexity and word error rate (WER) com- pared to conventional n-gram language mod- ...layer. Deep neural ... See full document
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Cross Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single Corpus Evaluation Enough?
... GEC models. The performance of four recent models, namely three neural machine translation (NMT)- based models (LSTM, CNN, and transformer) and a statistical machine ... See full document
6
Exploiting Deep Representations for Neural Machine Translation
... Dataset. To compare with the results reported by previous work (Gehring et al., 2017; Vaswani et al., 2017; Hassan et al., 2018), we conducted experiments on both Chinese⇒English (Zh⇒En) and English⇒German (En⇒De) ... See full document
10
Pragmatic Neural Language Modelling in Machine Translation
... n-gram models using intrinsic evaluations of heldout perplexity (Chelba et ...traditional models in natural language systems such as speech recognizers (Mikolov et ...2007). Neural ... See full document
10
Coverage Embedding Models for Neural Machine Translation
... Neural machine translation (NMT) has gained pop- ularity in recent years (e.g. (Bahdanau et al., 2014; Jean et al., 2015; Luong et al., 2015; Mi et al., 2016b; Li et al., 2016)), especially for the ... See full document
6
Syntactically Supervised Transformers for Faster Neural Machine Translation
... the translation quality (in terms of BLEU) and the decoding speedup (average time to decode a sentence) of SynST compared to com- peting ...Fr language pairs), 6 we find that SynST achieves a strong balance ... See full document
13
Statistical Machine Translation with Local Language Models
... local language modeling approach with a decoder is ...word language model ...many language models, local language models can be added using the same func- tionalities of SRILM’s ... See full document
11
Semantic Language models with deep neural Networks
... these models can be optimized either for recognition or for understanding by changing the amount of semantic in- formation we place in the ...these models can be adapted to the dialog context by means of ... See full document
182
Deep Neural Machine Translation with Linear Associative Unit
... studying Deep Neural Net- works ...that deep architectures in both the encoder and decoder are essential for cap- turing subtle irregularities in the source and tar- get ...a deep neu- ral ... See full document
10
Neural Network Language Models for Candidate Scoring in Hybrid Multi System Machine Translation
... Multi-system machine translation (MT) is a subset of hybrid MT where multiple MT systems are com- bined in a single system in order to boost the accuracy and fluency of the ...statistical language ... See full document
8
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
Converting Continuous Space Language Models into N Gram Language Models for Statistical Machine Translation
... network language models, or continuous-space language models (CSLMs), have been shown to improve the performance of statistical machine translation (SMT) when they are used for ... See full document
6
Low Resource Corpus Filtering Using Multilingual Sentence Embeddings
... uses language model and word trans- lation scores, with weights optimized to separate clean and synthetic noise ...Zipporah models for both language pairs Sinhala–English and ...(probabilistic ... See full document
6
Data Augmentation for Low Resource Neural Machine Translation
... In computer vision, data augmentation techniques are widely used to increase robustness and im- prove learning of objects with a limited number of training examples. In image processing the train- ing data is augmented ... See full document
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