[PDF] Top 20 Phrase Based Statistical Language Generation Using Graphical Models and Active Learning
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Phrase Based Statistical Language Generation Using Graphical Models and Active Learning
... sation phrase can easily be conditioned on syntac- tic constructs governing that phrase, and the recur- sive nature of syntax can be modelled by keeping track of the depth of the current embedded ...by ... See full document
10
Statistical Phrase Based Translation
... new phrase-based translation model and decoding algorithm that enables us to evaluate and compare several, previ- ously proposed phrase-based translation mod- ...why phrase-based ... See full document
7
Continuous Space Translation Models for Phrase Based Statistical Machine Translation
... for language modeling, there were also two attempts to apply the same ideas to the translation ...e.g. based on bilingual ...a phrase-based SMT ...machine learning, we are generally not ... See full document
10
Active Learning for Statistical Natural Language Parsing
... language models. The major contribution of this paper is that a model- based distance measure is proposed and used in active ...the active train- ing set and a sample is then selected ... See full document
8
Active Learning for Statistical Phrase based Machine Translation
... Despite the promise of active learning for SMT for domain adaptation and low-density/low-resource languages, there has been very little work published on this issue. A Ph.D. proposal by Chris Callison- ... See full document
9
Statistical Models for Unsupervised Prepositional Phrase Attachment
... Heuristic Extraction of Unambiguous Cases Given a tagged and chunked sentence, the extraction heuristic returns head word tuples of the form v,p, n2 or n,p, n2, where v is the verb, n is[r] ... See full document
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Statistical Models for Unsupervised Prepositional Phrase Attachment
... Statistical Models for Unsupervised Prepositional Phrase Attachment S t a t i s t i c a l M o d e l s for U n s u p e r v i s e d P r e p o s i t i o n a l P h r a s e A t t a c h m e n t A d w a i t[.] ... See full document
7
Adapting Translation Models to Translationese Improves SMT
... T phrase tables (which are based on a parallel corpus whose source is original texts, and whose target is trans- lationese) to have more unique source phrases and a lower number of translations per source ... See full document
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Automated Grammar Correction Using Hierarchical Phrase Based Statistical Machine Translation
... corpus of incorrect and correct sentences. The system starts with an alignment to obtain word to word translation probabilities. The second stage is grammar extraction using the hiero style of gram- mar (Chiang, ... See full document
5
Learning to Automatically Generate Fill In The Blank Quizzes
... Natural Language Processing, deep models have succeeded in large part because they learn and use their own continuous numeric representa- tional systems for words and ...our models start with random ... See full document
5
Learning Graphical Models With Hubs
... Gaussian graphical model (Liu and Ihler, 2011; Defazio and Caetano, ...Gaussian graphical models (Hero and Rajaratnam, 2012; Firouzi and Hero, ... See full document
35
Learning Syntactic Verb Frames using Graphical Models
... Our study reached two important conclusions: first, given the same data as input, an unsupervised prob- abilistic model can outperform a hand-crafted rule- based SCF extractor with a predefined inventory. We ... See full document
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Learning Phrase Boundaries for Hierarchical Phrase based Translation
... Hierarchical phrase-based models pro- vide a powerful mechanism to capture non-local phrase reorderings for statis- tical machine translation ...many phrase reorderings are arbi- trary ... See full document
8
Discriminative Phrase based Lexicalized Reordering Models using Weighted Reordering Graphs
... that phrase- based models do not perform as well, although the difference in BLEU is only ...reordering models yield better re- ordering estimates, since considering longer train- ing ... See full document
9
Learning Composition Models for Phrase Embeddings
... features based on combining word embeddings and contex- tual information (Nguyen and Grishman, 2014; Roth and Woodsend, 2014; Kiros et ...to phrase semantics include Socher et ...with phrase types ... See full document
16
Integration of Reduplicated Multiword Expressions and Named Entities in a Phrase Based Statistical Machine Translation System
... 2010) using support vector machine (SVM) based machine learning ...SVM based RMWE identification system shows recall, precision and F-Score of ... See full document
9
The IIT Bombay Hindi English Translation System at WMT 2014
... divergence using preordering ...rules using shallow parsing infor- ...(1) generation of ar- ticles, which Hindi lacks, (2) heavy overloading of English prepositions, making it difficult to predict ... See full document
7
Learning Word Reorderings for Hierarchical Phrase based Statistical Machine Translation
... of using word reordering information within hierarchical phrase-based SMT by integrat- ing Tromble and Eisner (2009)’s word reordering model into decoder as a feature, which estimates the probability ... See full document
7
Node-Based Learning of Multiple Gaussian Graphical Models
... The ADMM algorithms presented in the previous section work well on problems of moder- ate size. In order to solve the PNJGL or CNJGL optimization problems when the number of variables is large, a faster approach is ... See full document
44
Towards technology assisted co construction with communication partners
... prediction, phrase pre- diction, in fact whole utterance prediction can fol- low, driven by Kim’s intuitions derived from knowl- edge of Sandy, true sensitivity to context, topic, so- cial protocol, ... See full document
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