Section 3 introduces two probabilistic models for integrating translations and transliterations into a translation model which are based on conditional and joint probability distributions.

Both of our models combine a character-based transliteration model with a word-based translation model .

Language Model for Unknown Words: Our model generates transliterations that can be known or unknown to the language model and the translation model .

We refer to the words known to the language model and to the translation model as LM-known and TM-known words respectively and to words that are unknown as LM-unknown and TM-unknown respectively.

Moreover, they are working with a large bitext so they can rely on their translation model and only need to transliterate NEs and OOVs.

Our translation model is based on data which is both sparse and noisy.

Similarity scores are used as additional features of the translation model to improve translation performance.

The sense similarity scores are used as feature functions in the translation model .

In particular, all the allowed bilingual corpora except the UN corpus and Hong Kong Hansard corpus have been used for estimating the translation model .

The second one is the small data condition where only the FBIS3 corpus is used to train the translation model .

The hierarchical phrase-based translation method (Chiang, 2005; Chiang, 2007) is a formal syntax-based translation modeling method; its translation model is a weighted synchronous context free grammar (SCFG).

the translation probabilities in a translation model , for units from parallel corpora are mainly based on the co-occurrence counts of the two units.

, N is used for both the initialization of the translation model p(f|é) and the phrase model training.

We have shown that training phrase models can improve translation performance on a state-of-the-art phrase-based translation model .

The scaling factors of the translation models have been optimized for BLEU on the DEV data.

We will focus on the proposed leaving-one-out technique and show that it helps in finding good phrasal alignments on the training data that lead to improved translation models .

Viterbi Word Alignment Phrase Alignment word translation models phrase translation models trained by EM Algorithm trained by EM Algorithm heuristic phrase phrase translation counts probabilities Phrase Translation Table ‘ ‘ Phrase Translation Table

This is different from word-based translation models , where a typical assumption is that each target word corresponds to only one source word.

We thus propose to combine the advantages of both, and present a novel constituency-to-dependency translation model , which uses constituency forests on the source side to direct the translation, and dependency trees on the target side (as a language model) to ensure grammaticality.

In this paper, we presented a novel forest-based constituency-to-dependency translation model , which combines the advantages of both tree-to-string and string-to-tree systems, runs fast and guarantees grammaticality of the output.

Linguistically syntax-based statistical machine translation models have made promising progress in recent years.

Figure 1 shows a word-aligned source constituency forest FC and target dependency tree De, our constituency to dependency translation model can be formalized as:

(2009), we apply forest into a new constituency tree to dependency tree translation model rather than constituency tree-to-tree model.

IBM Model 1 only employs the word translation model to calculate the probabilities of alignments.

In IBM Model 2, both the word translation model and position distribution model are used.

IBM Model 3, 4 and 5 consider the fertility model in addition to the word translation model and position distribution model.

These tree-to-tree rules are applicable for forest-to-tree translation models (Liu et al., 2009a).

4.1 Translation models

In our translation models , we have made use of three kinds of translation rule sets which are trained separately.