Our proposed method employs statistical machine translation to improve question retrieval and enriches the question representation with the translated words from other languages via matrix factorization.

Row 8 and row 9 are our proposed method, which leverages statistical machine translation to improve question retrieval via matrix factorization.

The idea of improving question retrieval with statistical machine translation is based on the following two observa-

However, there are two problems with this enrichment: (1) enriching the original questions with the translated words from other languages increases the dimensionality and makes the question representation even more sparse; (2) statistical machine translation may introduce noise, which can harm the performance of question retrieval.

To solve these two problems, we propose to leverage statistical machine translation to improve question retrieval via matrix factorization.

This paper aims to leverage statistical machine translation to enrich the question representation.

Statistical machine translation (e.g., Google Translate) can utilize contextual information during the question translation, so it can solve the word ambiguity and word mismatch problems to some extent.

However, there are two problems with this enrichment: (1) enriching the original questions with the translated words from other languages makes the question representation even more sparse; (2) statistical machine translation may introduce noise.5 To solve these two problems, we propose to leverage statistical machine translation to improve question retrieval via matrix factorization.

Statistical machine translation into a morphologically complex language such as Turkish, Finnish or Arabic, involves the generation of target words with the proper morphology, in addition to properly ordering the target words.

We assume that the reader is familiar with the basics of phrase-based statistical machine translation (Koehn et al., 2003) and factored statistical machine translation (Koehn and Hoang, 2007).

Statistical Machine Translation into a morphologically rich language is a challenging problem in that, on the target side, the decoder needs to generate both the right sequence of constituents and the right sequence of morphemes for each word.

Using morphology in statistical machine translation has been addressed by many researchers for translation from or into morphologically rich(er) languages.

Goldwater and McClosky (2005) use morphological analysis on the Czech side to get improvements in Czech-to-English statistical machine translation .

A simple statistical machine translation method, word-by-word decoding, where not a parallel corpus but a bilingual lexicon is necessary, is adopted for the treebank translation.

A simple statistical machine translation technique, word-by-word decoding, where only a bilingual lexicon is necessary, is used to translate the source treebank.

In addition, a standard statistical machine translation method based on a parallel corpus will not work effectively if it is not able to find a parallel corpus that right covers source and target treebanks.

The second is that a parallel corpus is required for their work and a strict statistical machine translation procedure was performed, while our approach holds a merit of simplicity as only a bilingual lexicon is required.

A word-by-word statistical machine translation strategy is adopted to translate words attached with the respective dependency information from the source language to the target one.

We then apply the algorithms to statistical machine translation by computing the sense similarity between the source and target side of translation rule pairs.

similarity for terms from parallel corpora and applied it to statistical machine translation .

We have shown that the sense similarity computed between units from parallel corpora by means of our algorithm is helpful for at least one multilingual application: statistical machine translation .

Is it useful for multilingual applications, such as statistical machine translation (SMT)?

Second, we use the sense similarities between the source and target sides of a translation rule to improve statistical machine translation performance.

The systematic word order difference between two languages poses a challenge for current statistical machine translation (SMT) systems.

:15 for Statistical Machine Translation

The remainder of this paper is organized as follows: Section 2 introduces the basement of this research: the principle of statistical machine translation .

In statistical machine translation , we are given a source language sentence fi] = fl... fj .

We show that combining them with word—based n—gram models in the log—linear model of a state—of—the—art statistical machine translation system leads to improvements in translation quality as indicated by the BLEU score.

In the subsequent experiments, we use a phrase-based statistical machine translation system based on the log-linear formulation of the problem described in (Och and Ney, 2002):

However, in the area of statistical machine translation , especially in the context of large training corpora, fewer experiments with class-based n-gram models have been performed with mixed success (Raab, 2006).

We then show that using partially class-based language models trained using the resulting classifications together with word-based language models in a state-of-the-art statistical machine translation system yields improvements despite the very large size of the word-based models used.

Data selection has been demonstrated to be an effective approach to addressing the lack of high-quality bitext for statistical machine translation in the domain of interest.

our methods into domain adaptation task of statistical machine translation in model level.

Statistical machine translation depends heavily on large scale parallel corpora.

Translation model is a key component in statistical machine translation .

This paper proposes to use monolingual collocations to improve Statistical Machine Translation (SMT).

Statistical Machine Translation .

The Mathematics of Statistical Machine Translation : Pa-

Moses: Open Source Toolkit for Statistical Machine Translation .

Currently, almost all of the statistical machine translation (SMT) models are trained with the parallel corpora in some specific domains.

During the last decade, statistical machine translation has made great progress.

Recently, more and more researchers concentrated on taking full advantage of the monolingual corpora in both source and target languages, and proposed methods for bilingual lexicon induction from nonparallel data (Rapp, 1995, 1999; Koehn and Knight, 2002; Haghighi et al., 2008; Daume III and J agarlamudi, 2011) and proposed unsupervised statistical machine translation (bilingual lexicon is a byproduct) with only monolingual corpora (Ravi and Knight, 2011; Nuhn et al., 2012; Dou and Knight, 2012).

The unsupervised statistical machine translation method (Ravi and Knight, 2011; Nuhn et al., 2012; Dou and Knight, 2012) viewed the translation task as a decipherment problem and designed a generative model with the objective function to maximize the likelihood of the source language monolingual data.

Early work in statistical machine translation Viewed translation as a noisy channel process comprised of a translation model, which functioned to posit adequate translations of source language words, and a target language model, which guided the fluency of generated target language strings (Brown et al.,

Drawing on earlier successes in speech recognition, research in statistical machine translation has effectively used n-gram word sequence models as language models.

speech recognition and statistical machine translation focus on the use of n-grams, which provide a simple finite-state model approximation of the target language.

priate algorithmic fit for incorporating syntax into phrase-based statistical machine translation , since both process sentences in an incremental left-to-right fashion.

The factored statistical machine translation model uses a log-linear approach, in order to combine the several components, including the language model, the reordering model, the translation models and the generation models.

Traditional statistical machine translation methods are based on mapping on the lexical level, which takes place in a local window of a few words.

Our method is based on factored phrase-based statistical machine translation models.

Traditional statistical machine translation models deal with this problems in two ways:

However, these solutions are impractical in complex structured prediction problems such as statistical machine translation .

Finally, although motivated by statistical machine translation , RM is a gradient-based method that can easily be applied to other problems.

The desire to incorporate high-dimensional sparse feature representations into statistical machine translation (SMT) models has driven recent research away from Minimum Error Rate Training (MERT) (Och, 2003), and toward other discriminative methods that can optimize more features.

Some Statistical Machine Translation systems never see the light because the owner of the appropriate training data cannot release them, and the potential user of the system cannot disclose what should be translated.

It is generally taken for granted that whoever is deploying a Statistical Machine Translation (SMT) system has unrestricted rights to access and use the parallel data required for its training.

private access to a phrase table or other resources for the purpose of performing statistical machine translation .

In this paper, we propose a new Bayesian inference method to train statistical machine translation systems using only nonparallel corpora.

Statistical machine translation (SMT) systems these days are built using large amounts of bilingual parallel corpora.

Recently, this topic has been receiving increasing attention from researchers and new methods have been proposed to train statistical machine translation models using only monolingual data in the source and target language.

In particular, we use topic models to aid statistical machine translation (Koehn, 2009, SMT).

2.1 Statistical Machine Translation

Statistical machine translation casts machine translation as a probabilistic process (Koehn, 2009).

In this paper, we propose a sense-based translation model to integrate word senses into statistical machine translation .

Therefore a natural assumption is that word sense disambiguation (WSD) may contribute to statistical machine translation (SMT) by providing appropriate word senses for target translation selection with context features (Carpuat and Wu, 2005).

'or Statistical Machine Translation

In this paper, we report our preliminary efforts in building an English-Turkish parallel treebank corpus for statistical machine translation .

For example, EuroParl corpus (Koehn, 2002), one of the biggest parallel corpora in statistical machine translation , contains 22 languages (but not Turkish).

In this study, we report our preliminary efforts in constructing an English-Turkish parallel treebank corpus for statistical machine translation .

Predicate-argument structure contains rich semantic information of which statistical machine translation hasn’t taken full advantage.

In this paper, we propose two discriminative, feature-based models to exploit predicate-argument structures for statistical machine translation : 1) a predicate translation model and 2) an argument reordering model.

Recent years have witnessed increasing efforts towards integrating predicate-argument structures into statistical machine translation (SMT) (Wu and Fung, 2009b; Liu and Gildea, 2010).

Statistical machine translation is often faced with the problem of combining training data from many diverse sources into a single translation model which then has to translate sentences in a new domain.

Statistical machine translation (SMT) systems require large parallel corpora in order to be able to obtain a reasonable translation quality.

els in Statistical Machine Translation

In this paper we show how to train statistical machine translation systems on real-life tasks using only nonparallel monolingual data from two languages.

We presented a method for learning statistical machine translation models from nonparallel data.

This work serves as a big step towards large-scale unsupervised training for statistical machine translation systems.

Parallel data in the domain of interest is the key resource when training a statistical machine translation (SMT) system for a specific purpose.

The ability to predict the amount of parallel data required to achieve a given level of quality is very valuable in planning business deployments of statistical machine translation ; yet, we are not aware of any rigorous proposal for addressing this need.

Parallel data in the domain of interest is the key resource when training a statistical machine translation (SMT) system for a specific business purpose.

Several attempts have been made to learn phrase translation probabilities for phrase-based statistical machine translation that go beyond pure counting of phrases in word-aligned training data.

We conducted our experiments on the German-English data published for the ACL 2008 Workshop on Statistical Machine Translation (WMT08).

Europarl task from the ACL 2008 Workshop on Statistical Machine Translation (WMT08).

Minimum Error Rate Training (MERT) and Minimum Bayes-Risk (MBR) decoding are used in most current state-of—the-art Statistical Machine Translation (SMT) systems.

Statistical Machine Translation (SMT) systems have improved considerably by directly using the error criterion in both training and decoding.

In the context of statistical machine translation , the optimization procedure was first described in Och (2003) for N -best lists and later extended to phrase-lattices in Macherey et al.

Statistical machine translation (SMT) models require bilingual corpora for training, and these corpora are often multilingual with parallel text in multiple languages simultaneously.

The main source of training data for statistical machine translation (SMT) models is a parallel corpus.

11 Statistical Machine Translation*

Conventional statistical machine translation (SMT) systems do not perform well on measure word generation due to data sparseness and the potential long distance dependency between measure words and their corresponding head words.

Our model works as a postprocessing procedure over output of statistical machine translation systems, and can work with any SMT system.

In most statistical machine translation (SMT) models (Och et al., 2004; Koehn et al., 2003; Chiang, 2005), some of measure words can be generated without modification or additional processing.

In this paper, we propose a novel string-to-dependency algorithm for statistical machine translation .

In this paper, we propose a novel string-to-dependency algorithm for statistical machine translation .

In recent years, hierarchical methods have been successfully applied to Statistical Machine Translation (Graehl and Knight, 2004; Chiang, 2005; Ding and Palmer, 2005; Quirk et al., 2005).

We present a method to transliterate names in the framework of end-to-end statistical machine translation .

We have shown that a state-of-the-art statistical machine translation system can benefit from a dedicated transliteration module to improve the transla-

State-of-the-art statistical machine translation (SMT) is bad at translating names that are not very common, particularly across languages with different character sets and sound systems.