[PDF] Top 20 A Localized Prediction Model for Statistical Machine Translation

... over localized models such as ...current localized model, successor blocks of dif- ferent sizes are directly compared to each other, which is intuitively not the best approach ...the localized ... See full document

... a model would account for this ambiguity by marginalising out the derivations, thus predicting the best transla- tion rather than the best ...simple model and feature structures, such that spurious am- ... See full document

... Phrase translation rules (up to a maximum source span of 5 words) were extracted from a combination of forward and backward word alignments (Koehn et ...log-linear model that com- bines numerous features, ... See full document

... tion model on the FBIS corpus (7.2M+9.2M words); for the language model, we used the SRI Language Modeling Toolkit to train a trigram model with mod- ified Kneser-Ney smoothing (Chen and Goodman, ... See full document

... In this paper, we propose a new instance weight- ing approach to domain adaptation based on a vec- tor space model (VSM). As in (Foster et al., 2010), this approach works at the level of phrase pairs. However, the ... See full document

... language model is a good complement for n-gram model to further im- prove translation ...our model can capture n-gram level topic information, rather than only focus on estimating 1-gram ... See full document

... Compared to previous lexical selection models, CATM jointly models both local contextual words and global topics. Such a joint modeling also en- ables CATM to capture their inner correlations at the model level. ... See full document

... A pessimistic interpretation of the results would point out that performance gains compared to the best baseline system are modest or even inexistent in some settings. However, we want to stress two important points. ... See full document

... topic model — HTMM which has different assumption from PLSA and LDA; (2) rather than modeling topic-dependent translation lexicons in the training process, we estimate topic-specific lexical probability by ... See full document

... 2013), for which learning does not converge. It is important to note that the goal of our ex- periments is not to present improvements of coac- tive learning over the “optimal” full-information model in terms of ... See full document

... SMT model similar to Chiang (2005). The trigram target language model is trained from the Xinhua portion of English Gi- gaword corpus (Graff and Cieri, ...for translation performance evaluation is ... See full document

... composition model, re- ducing the ambiguity of the expanded model by the statistics of hypotheses while ...the translation model (Brown et ... See full document

... both translation and evaluation. At translation time, domain adaptation techniques increase the likelihood of correct translations on average, but they do not provide the MT system with any inform- ation to ... See full document

... Traditionally, statistical machine translation systems have relied on parallel bi-lingual data to train a translation ...language model in the target ...a statistical ... See full document

... pseudo translation rule accord- ing to the word order of the head-dependents rela- ...any translation rule about to “(2010 年 ) (FIFA) 世 界杯 ”, we will construct a pseudo translation rule “(x 1 :2010 年 ... See full document

... Table 2: Precision and Recall of MTS The precision (1-best accuracy) of the translit- eration model is quite low. This is because the transliteration corpus is noisy and contains imper- fect transliteration pairs. ... See full document

... In order to train these classifiers, we have to col- lect training events from our word-aligned bilin- gual training data where source words are anno- tated with their corresponding sense clusters pre- dicted by the ... See full document

... language model interpo- lation into the decoding phase is far greater than for translation models, since the number of hy- potheses that need to be evaluated by the language model is several orders ... See full document

... baseline model features, we first use those from Hatori and Suzuki (2011): the bidirectional translation probabilities, P (t | s) and P (s | t), the target character n-gram probability, P (t), the tar- get ... See full document

... Statistical Machine Translation (SMT) is attract- ing more attentions than rule-based and example- based methods because of the availability of large training corpora and automatic ...to-tree ... See full document