They can be obtained by training statistical translation models on parallel monolingual corpora, such as question-answer pairs, where answers act as the “source” language and questions as the “target” language.

We compare monolingual translation models built from lexical semantic resources with two other kinds of datasets: manually-tagged question reformulations and question-answer pairs.

Berger and Lafferty (1999) have formulated a further solution to the lexical gap problem consisting in integrating monolingual statistical translation models in the retrieval process.

Monolingual translation models encode statistical word associations which are trained on parallel monolingual corpora.

While collection-specific translation models effectively encode statistical word associations for the target document collection, it also introduces a bias in the evaluation and makes it difficult to assess the quality of the translation model per se, independently from a specific task and document collection.

For the synthetic method, we used the ES translation model to translate the English part of the CE corpus to Spanish to construct a synthetic corpus.

And we also used the BTEC CEl corpus to build a EC translation model to translate the English part of ES corpus into Chinese.

Then we combined these two synthetic corpora to build a Chinese-Spanish translation model .

It multiples corresponding translation probabilities and lexical weights in source-pivot and pivot-target translation models to induce a new source-target phrase table.

For example, we can obtain a source-pivot corpus by translating the pivot sentence in the source-pivot corpus into the target language with pivot-target translation models .

Following the method described in Wu and Wang (2007), we train the source-pivot and pivot-target translation models using the source-pivot and pivot-target corpora, respectively.

Based on these two models, we induce a source-target translation model , in which two important elements need to be induced: phrase translation probability and lexical weight.

Then we build a source-target translation model using this corpus.

The source-target pairs extracted from this synthetic multilingual corpus can be used to build a source-target translation model .

This paper proposes a forest-based tree sequence to string translation model for syntax-based statistical machine translation, which automatically learns tree sequence to string translation rules from word-aligned source-side-parsed bilingual texts.

The proposed model leverages on the strengths of both tree sequence-based and forest-based translation models .

4) Decode the translation forest using our translation model and a dynamic search algorithm.

3.3 Forest-based tree-sequence to string translation model

Given a source forest F and target translation T S as well as word alignment A, our translation model is formulated as:

to String Translation Model

Section 2 describes related work while section 3 defines our translation model .

Motivated by the fact that non-syntactic phrases make nontrivial contribution to phrase-based SMT, the tree sequence-based translation model is proposed (Liu et al., 2007; Zhang et al., 2008a) that uses tree sequence as the basic translation unit, rather than using single subtree as in the STSG.

(2007) propose the tree sequence concept and design a tree sequence to string translation model .

(2008a) propose a tree sequence-based tree to tree translation model and Zhang et al.

Current SMT systems usually decode with single translation models and cannot benefit from the strengths of other models in decoding phase.

We instead propose joint decoding, a method that combines multiple translation models in one decoder.

We have presented a framework for including multiple translation models in one decoder.

In this paper, we propose a framework for combining multiple translation models directly in de-

Second, translation models differ in decoding algorithms.

Despite the diversity of translation models , they all have to produce partial translations for substrings of input sentences.

Therefore, we represent the search space of a translation model as a structure called translation hypergraph.

The tree sequence based translation model allows the violation of syntactic boundaries in a rule to capture non-syntactic phrases, where a tree sequence is a contiguous sequence of sub-trees.

This paper goes further to present a translation model based on noncontiguous tree sequence alignment, where a noncontiguous tree sequence is a sequence of sub-trees and gaps.

In the experiments, we train the translation model on FBIS corpus (7.2M (Chinese) + 9.2M (English) words) and train a 4-gram language model on the Xinhua portion of the English Gigaword corpus (181M words) using the SRILM Toolkits (Stolcke,

Bod (2007) also finds that discontinues phrasal rules make significant improvement in linguistically motivated STSG-based translation model .

2 We illustrate the rule extraction with an example from the tree-to-tree translation model based on tree sequence alignment (Zhang et al, 2008a) without losing of generality to most syntactic tree based models.

To address this issue, we propose a syntactic translation model based on noncontiguous tree sequence alignment.

In this section, we give a formal definition of SncTSSG and accordingly we propose the alignment based translation model .

2.2 SncTSSG based Translation Model

In order to evaluate the influence of segmentation results upon the statistical ON translation system, we compare the results of two translation models .

For constructing a statistical ON translation model , we use GIZA++I to align the Chinese NEs and the English NEs in the training set.

Q2: the Chinese ON and the results of the statistical translation model .

The first type of methods translates ONs by building a statistical translation model .

The statistical translation model can give an output for any input.

The performance of the statistical ON translation model is dependent on the precision of the Chinese ON segmentation to some extent.

The weight of h is the incremental score contributed to all translations containing the rule application, including translation model features on 7“ and language model features that depend on both 7“ and the English contexts of the child nodes.

Hiero is a hierarchical system that expresses its translation model as a synchronous context-free grammar (Chiang, 2007).

SBMT is a string-to-tree translation system with rich target-side syntactic information encoded in the translation model .

Figure 3: N - grams with high expected count are more likely to appear in the reference translation that 71- grams in the translation model’s Viterbi translation, 6*.

The quality of the parallel data and the word alignment have significant impacts on the learned translation models and ultimately the quality of translation output.

The source-to-target lexical translation model p(t|s) and target-to-source model p(s|t) can be obtained through IBM Model-l or HMM training.

For the efficient computation of the denominator, we use the lexical translation model .

Our translation model was trained on about 1M parallel sentence pairs (about 28M words in each language), which are sub-sampled from corpora distributed by LDC for the NIST MT evaluation using a sampling method based on the n-gram matches between training and test sets in the foreign side.

We use GIZA++ (Och and Ney, 2000), a suffix-array (Lopez, 2007), SRILM (Stol-cke, 2002), and risk-based deterministic annealing (Smith and Eisner, 2006)17 to obtain word alignments, translation models , language models, and the optimal weights for combining these models, respectively.

However, suppose the hypergraph were very large (thanks to a large or smoothed translation model and weak pruning).

We want to further study the happenings after we integrate the constraint feature (our SDB model and Marton and Resnik’s XP+) into the log-linear translation model .

All translation models were trained on the FBIS corpus.

new feature into the log-linear translation model : PSDB (b|T, This feature is computed by the SDB model described in equation (3) or equation (4), which estimates a probability that a source span is to be translated as a unit within particular syntactic contexts.