[PDF] Top 20 N gram based Tense Models for Statistical Machine Translation
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N gram based Tense Models for Statistical Machine Translation
... document-level tense model seems more effective than the sentence-level ...main tense distributions of one reference. The main tense distributions for the baseline and our pro- posed system are ... See full document
10
Continuous Space Translation Models for Phrase Based Statistical Machine Translation
... likely translation provided by the ...full translation process must completely rely on the LM to select the best individual translations of the three words so that a correct French sentence will be ... See full document
10
Bilingual Cluster Based Models for Statistical Machine Translation
... Statistical models, such as n-gram models, are widely used in natural language processing, for ex- ample in speech recognition and statistical machine translation ... See full document
10
From n gram based to CRF based Translation Models
... instances. Machine translation, like most NLP tasks, does not easily lend itself to that approach, due to the complexity of the input/output objects (word or la- bel strings, parse trees, dependency ... See full document
12
LIMSI@WMT’16: Machine Translation of News
... the n-gram translation models and target n- gram language models, 13 conventional features are combined: 4 lexicon models similar to the ones used in standard ... See full document
7
Syntax Based Word Ordering Incorporating a Large Scale Language Model
... for statistical machine ...an N-gram language model, which has been used by text generation systems to improve ...an N-gram model by applying on- line large-margin ... See full document
11
Stream based Translation Models for Statistical Machine Translation
... the translation and alignment probabilities for the HMM-based alignments, we employ the EM algo- rithm via dynamic ...complex models are defined, IBM Models 2 to Model 6 (Brown et ...or ... See full document
9
Distortion Models for Statistical Machine Translation
... word reorderings during the translation process. Trying all possible word reordering is an NP-Complete prob- lem as shown in (Knight, 1999), which makes search- ing for the optimal solution among all possible ... See full document
8
Statistical models for text normalization and machine translation
... First, there is not complete agreement with respect to what “standard Irish” means. The first movement toward standardization of the written language goes back to the 1930’s with the establishment of govern- ment ... See full document
8
Statistical Machine Translation with Local Language Models
... language models depends among other things on the size of the parallel cor- pus, the size and order of the word language model, and whether lexicalized distortion models are ...guage models without ... See full document
11
Statistical Machine Translation Models for Personalized Search
... size fits all”: the decision of which documents to re- trieve is made based only on the query posed, with- out consideration of a particular user’s preferences and search context. When a query (e.g. “jaguar”) is ... See full document
8
Advancements in Reordering Models for Statistical Machine Translation
... are better than their corresponding baseline values with more than 95% confidence. We also compare our method with several other popular reorder- ing models. Our model ranks the second position which is slightly ... See full document
11
Evaluation of Language Models over Croatian Newspaper Texts
... character-based n-gram classifier that identifies loanwords or transliterated foreign words in the Ko- rean language as well as a pilot model for Japanese is developed ...classical models ... See full document
34
Mixing Multiple Translation Models in Statistical Machine Translation
... multiple translation models with multiple lan- guage models in ensemble ...using n-gram features, tuning using forest-based MERT, among other pos- sible ... See full document
10
Example based Machine Translation Based on Syntactic Transfer with Statistical Models
... and translation) are obtained from child ...the n-best sequences are se- lected. These n-best sequences and their prob- abilities are reused to calculate the probabilities of parent ...the ... See full document
7
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring
... 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 et ...chine translation ... See full document
8
The Operation Sequence Model—Combining N Gram Based and Phrase Based Statistical Machine Translation
... the translation accuracy drop because of search errors (Koehn et ...distortion models to achieve better translation accuracy than the baseline phrase-based system for a distortion limit of 15 ... See full document
30
N Gram Based Statistical Machine Translation versus Syntax Augmented Machine Translation: Comparison and System Combination
... to Machine Translation (MT): the CMU-UKA Syntax Augmented Machine Translation system (SAMT) and UPC-TALP N-gram-based Statistical Ma- chine Translation ... See full document
9
Grammatical Machine Translation
... evaluation on randomly selected 500 examples that were in coverage of the grammar-based generator. Two independent human judges were presented with the source sentence, and the output of the phrase- based ... See full document
8
Converting Continuous Space Language Models into N Gram Language Models for Statistical Machine Translation
... 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 reranking ... See full document
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