[PDF] Top 20 Converting Continuous Space Language Models into N Gram Language Models for Statistical Machine Translation
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Converting Continuous Space Language Models into N Gram Language Models for Statistical Machine Translation
... Because BNLMs can be trained from much larger corpora than those that can be used for training CSLMs, improving a BNLM by using a CSLM trained from a smaller corpus is very important. Actually, a CSLM trained from a ... See full document
6
Enhancing Language Models in Statistical Machine Translation with Backward N grams and Mutual Information Triggers
... two models to enhance the abil- ity of standard n-gram language models in captur- ing richer contexts and long-distance dependencies that go beyond the scope of forward ... See full document
10
Continuous Space Translation Models for Phrase Based Statistical Machine Translation
... the translation process (Zamora-Martínez et ...on continuous space translation models in an bilingual tuple system only used rescoring (Schwenk et ...the translation model is not ... See full document
10
Large and Diverse Language Models for Statistical Machine Translation
... See Figure 2 for an illustration that highlights what ratio of the LM is needed to translate a sin- gle sentence. The ratio increases roughly linear with sentence length. For a typical 30-word sen- tence, about 4% of the ... See full document
6
Multiple stream Language Models for Statistical Machine Translation
... online language models for translating multiple streams which naturally arise on the ...maintaining translation performance on all streams in small ...how translation perfor- mance can equal ... See full document
10
Deep Syntax Language Models and Statistical Machine Translation
... two models are and highlights the potential of the deep syntax language model to aid lexical choice in SMT ...tri- gram in the held-out data is <s> be also, where the word also is a sentential ... See full document
9
LIMSI@WMT’16: Machine Translation of News
... estimate n-gram models that use large output vocabulary, thereby making the training of large neural net- work language models feasible both for target lan- guage models (Le et ... See full document
7
Bilingual Structured Language Models for Statistical Machine Translation
... There is a variety of ways syntax can be used in a PBSMT model. Typically a syntactic represen- tation of a source sentence is used to define con- straints on the order in which the decoder trans- lates it. For example, ... See full document
11
N gram based Tense Models for Statistical Machine Translation
... natural language processing applications. However, most of current Statistical Machine Translation (SMT) systems mainly depend on translation model and language ...propose ... See full document
10
Large Language Models in Machine Translation
... 7-gram models with optimized stupid backoff factors for each order, while the learning curve presented here uses a fixed order of 5 and a single fixed backoff fac- ...of language model training data ... See full document
10
Dependency Based Bilingual Language Models for Reordering in Statistical Machine Translation
... bilingual language model ...bilingual models (Niehues et ...of translation experiments we performed a thor- ough comparison between various syntactically- enriched BiLMs and competing ...an ... See full document
12
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
Surveys: A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
... restrict translation hypotheses to well-formed target language sentences—ruling out, for instance, a translation that fails to reorder the translated verb renewed with respect to its ...both ... See full document
43
Letter N Gram based Input Encoding for Continuous Space Language Models
... standard n-gram language models (Bengio et ...network language mod- els on millions of words resulting in a decrease of the word error rate for continuous speech recog- ... See full document
10
Large, Pruned or Continuous Space Language Models on a GPU for Statistical Machine Translation
... a language model to evaluate the probability of the produced sequence of words, w and f respectively, we argue that the task of the lan- guage model is not exactly the same for both ap- ...best translation ... See full document
9
Investigating Continuous Space Language Models for Machine Translation Quality Estimation
... back-off n-gram language ...the continuous space and the training of the neural network is done by the stan- dard back-propagation algorithm and outputs are the converged posterior ... See full document
6
Continuous Space Language Models for Statistical Machine Translation
... automatic machine translation could be deployed and for which considerably less appropriate in-domain training data is ...records, translation systems for tourism related tasks or even any task for ... See full document
8
Statistical Machine Translation with Local Language Models
... local language models result in improvements over a competitive baseline, we designed the baseline to use a large 5-gram word language model and lexicalized distortion model- ing, both of ... See full document
11
Faster and Smaller N Gram Language Models
... the language model queries issued by the Joshua de- coder (Li et ...of language model queries in a cache should be effective at reducing overall language model ...given n-gram and its ... See full document
10
Learning to translate with products of novices: a suite of open ended challenge problems for teaching MT
... apply machine learning or feature engi- neering to the task of reranking the systems, so we provided several ...pro- gram computed the BLEU score on development data, while check ensured that a test ... See full document
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