[PDF] Top 20 Locally Training the Log Linear Model for SMT
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Locally Training the Log Linear Model for SMT
... the training corpus in both directions (Koehn et ...language model on the Xinhua portion of the English Gigaword cor- pus using the SRILM Toolkits (Stolcke, 2002) with modified Kneser-Ney smoothing (Chen ... See full document
10
Adaptive Development Data Selection for Log linear Model in Statistical Machine Translation
... adaptive SMT systems were discussed in the do- main adaptation context, in which one fundamen- tal idea is to estimate a more suitable domain- specific translation model or language ...existing ... See full document
9
Reinforcing the Topic of Embeddings with Theta Pure Dependence for Text Classification
... to model topical or sentiment ...the training of log-linear neural language model is based on local word dependen- cies ...itly model the word dependencies for those words that ... See full document
6
Stochastic Gradient Descent Training for L1 regularized Log linear Models with Cumulative Penalty
... whole training data (including the heldout data) and eval- uated the accuracy of the chunker on the test ...SGD training took much longer than OWL-QN because of the overhead of applying L1 penalty to all ... See full document
9
Training a Log Linear Parser with Loss Functions via Softmax Margin
... hybrid model of Clark and Curran (2007), which contains features over both normal- form derivations and CCG ...For training, we lim- ited the number of items in this chart to ...missive training ... See full document
11
Contrastive Estimation: Training Log Linear Models on Unlabeled Data
... train log-linear models, including conditional estimation (for the supervised case) and Riezler’s approximation (for the unsupervised ...in training implies a domain-specific set of examples which ... See full document
9
A Log Linear Block Transliteration Model based on Bi Stream HMMs
... specific model for Chinese-English name transliteration with clusterings of names’ origins, and appropriate hypotheses are generated given the ori- ...a SMT-framework. Technologies developed for SMT ... See full document
8
TriS: A Statistical Sentence Simplifier with Log linear Models and Margin based Discriminative Training
... We propose a statistical sentence simplifica- tion system with log-linear models. In contrast to state-of-the-art methods that drive sentence simplification process by hand-written linguis- tic rules, our ... See full document
9
Lexicalized Stochastic Modeling of Constraint Based Grammars using Log Linear Measures and EM Training
... of log-linear models from unannotated data (Riezler, ...estimate log-linear LFG models from large corpora of newspaper ...dierent training and test data and other evaluation ... See full document
8
Data driven learning of symbolic constraints for a log linear model in a phonological setting
... the model can learn a phonological system for the observed plurals. The model satisfies this goal if it predicts the observed forms at least as well as the baseline ...three model runs, and report ... See full document
10
Feature Rich Log Linear Lexical Model for Latent Variable PCFG Grammars
... We next investigate the effect of training the la- tent lexical model using the full feature set. Com- pared with the wid+full model, the full+full model improves 0.38 F on Arabic and 0.27 F ... See full document
9
Minimum Risk Annealing for Training Log Linear Models
... than either and in some cases significantly helped. Note, however, that annealed minimum risk train- ing results in a deterministic classifier just as these other training procedures do. The orthogonal technique ... See full document
8
Rapid estimation of nonlinear DSGE models
... Figure 2: Squared Euler errors for the growth model log scale, shown as a function of zt , calculated for various values of kt−1 , using linear black, quadratic blue and locally linear r[r] ... See full document
39
Log Linear Model for String Transformation Using Large Data Sets
... a log linear model, a training method for the model and an algorithm for generating the top k candidates using a non-dictionary approach which helps the approach to be accurate as well ... See full document
9
A Log Linear Model for Unsupervised Text Normalization
... this model cannot be trained in the standard supervised ...pervised training of locally-normalized conditional models (Berg-Kirkpatrick et ...new training approach using Monte Carlo tech- ... See full document
12
Perceptron Reranking for CCG Realization
... perceptron model can be used to achieve substantial improvements in re- alization quality with ...language model log probabilities as features in the model, which prior work on discriminative ... See full document
10
Improving MT System Using Extracted Parallel Fragments of Text from Comparable Corpora
... standard log-linear PB-SMT model as our baseline system: GIZA++ implementation of IBM word alignment model 4, phrase extraction heuristics described in (Koehn et ...minimum-error-rate ... See full document
8
Conditional Independence test for categorical data using Poisson log linear model
... All textbooks regarding categorical data analysis we came across, do mention the concept of independence and conditional independence. In addition, all of them have examples of testing whether two categorical variables ... See full document
10
Combining Unsupervised and Supervised Alignments for MT: An Empirical Study
... To establish strong baselines, we used a string-to- tree SMT system (Shen et al., 2008), one of the top performing systems in the NIST 2009 MT evalua- tion, and trained it with very large amounts of par- allel and ... See full document
7
Interpreting Questions with a Log Linear Ranking Model in a Virtual Patient Dialogue System
... a log-linear ranking model for in- terpreting questions in a virtual patient dia- logue system and demonstrate that it substan- tially outperforms a more typical multiclass classifier model ... See full document
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