Most existing work relies on machine translation engines to directly adapt labeled data from the source language to the target language.

This approach suffers from the limited coverage of vocabulary in the machine translation results.

Instead of using the corresponding machine translation of Chinese unlabeled sentences, we use the parallel English sentences of the Chinese unlabeled sentences.

One direct approach to leveraging the labeled data in English is to use machine translation engines as a black box to translate the labeled data from English to the target language (e.g.

Second, machine translation may change the sentiment polarity of the original text.

Instead of relying on the unreliable machine translated labeled data, CLMM leverages bilingual parallel data to bridge the language gap between the source language and the target language.

Most existing work relies on machine translation engines to directly adapt labeled data from the source language to target language.

Their bilingual view is also constructed by using machine translation engines to translate original documents.

Prettenhofer and Stein (2011) use machine translation engines in a different way.

We also show fluency improvements in a preliminary machine translation experiment.

We report machine translation reranking results in Section 5.4.

(2004) and Cherry and Quirk (2008) both use the l-best output of a machine translation system.

5.3.3 Machine Translation Classification

N -gram language models are a central component of all speech recognition and machine translation systems, and a great deal of research centers around refining models (Chen and Goodman, 1998), efficient storage (Pauls and Klein, 2011; Heafield, 2011), and integration into decoders (Koehn, 2004; Chiang, 2005).

We also show fluency improvements in a preliminary machine translation reranking experiment.

For machine translation , a model that builds target-side constituency parses, such as that of Galley et a1.

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.

Our experimental results show that ensemble decoding outperforms various strong baselines including mixture models, the current state-of-the-art for domain adaptation in machine translation .

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

As a result, language model adaptation has been well studied in various work (Clarkson and Robinson, 1997; Seymore and Rosenfeld, 1997; Bacchiani and Roark, 2003; Eck et al., 2004) both for speech recognition and for machine translation .

(2010) propose a similar method for machine translation that uses features to capture degrees of generality.

Unlike previous work on instance weighting in machine translation , they use phrase-level instances instead of sentences.

As the method highly depends on machine translation , a natural question arises to what is the impact when using different pivots or SMT systems.

The first part of iBLEU, which is the traditional BLEU score, helps to ensure the quality of the machine translation results.

We use 2003 NIST Open Machine Translation Evaluation data (NIST 2003) as development data (containing 919 sentences) for MERT and test the performance on NIST 2008 data set (containing 1357 sentences).

Paraphrasing technology has been applied in many NLP applications, such as machine translation (MT), question answering (QA), and natural language generation (NLG).

As paraphrasing can be viewed as a translation process between the original expression (as input) and the paraphrase results (as output), both in the same language, statistical machine translation (SMT) has been used for this task.

the noise introduced by machine translation , Zhao et al.

We focus on sentence level paraphrasing and leverage homogeneous machine translation systems for this task bi-directionally.

In this work, we introduce the TESLA-CELAB metric (Translation Evaluation of Sentences with Linear-programming-based Analysis — Character-level Evaluation for Languages with Ambiguous word Boundaries) for automatic machine translation evaluation.

Since the introduction of BLEU (Papineni et al., 2002), automatic machine translation (MT) evaluation has received a lot of research interest.

The Workshop on Statistical Machine Translation (WMT) hosts regular campaigns comparing different machine translation evaluation metrics (Callison-Burch et al., 2009; Callison-Burch et al., 2010; Callison-Burch et al., 2011).

The research on automatic machine translation evaluation is important for a number of reasons.

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.

(2008), the authors examined corpus features that contribute most to the machine translation performance.

In this paper, we address the issue for learning better translation consensus in machine translation (MT) research, and explore the search of translation consensus from similar, rather than the same, source sentences or their spans.

Experimental results show that, our method can significantly improve machine translation performance on both IWSLT and NIST data, compared with a state-of-the-art baseline.

To calculate consensus statistics, we develop a novel structured label propagation method for structured learning problems, such as machine translation .

G-Re-Rank-GC and G-Decode-GC improve the performance of machine translation according to the baseline.

In this paper, we demonstrate that accurate machine translation is possible without the concept of “words,” treating MT as a problem of transformation between character strings.

Traditionally, the task of statistical machine translation (SMT) is defined as translating a source sen-

boundaries, all machine translation systems perform at least some precursory form of tokenization, splitting punctuation and words to prevent the sparsity that would occur if punctuated and non-punctuated words were treated as different entities.

In this paper, we propose improvements to the alignment process tailored to character-based machine translation , and demonstrate that it is, in fact, possible to achieve translation accuracies that ap-

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.

In this work, we will develop, describe, and evaluate methods for large vocabulary unsupervised learning of machine translation models suitable for real-world tasks.

Long-span features, such as syntax, can improve language models for tasks such as speech recognition and machine translation .

Our results make long-span syntactic LMs practical for real-time ASR, and can potentially impact machine translation decoding as well.

Language models (LM) are crucial components in tasks that require the generation of coherent natural language text, such as automatic speech recognition (ASR) and machine translation (MT).

This independence means our tools are useful for other tasks, such as 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.

The two models are integrated into a state-of-the-art phrase-based machine translation system and evaluated on Chinese-to-English translation tasks with large-scale training data.

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

Long distance word reordering is a major challenge in statistical machine translation research.

We evaluated our approach on large-scale J apanese-English and English-Japanese machine translation tasks, and show that it can significantly outperform the baseline phrase-based SMT system.

Modeling word reordering between source and target sentences has been a research focus since the emerging of statistical machine translation .

We evaluated our approach on large-scale J apanese-English and English-Japanese machine translation tasks, and experimental results show that our approach can bring significant improvements to the baseline phrase-based SMT system in both pre-ordering and integrated decoding settings.

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 .

Discriminative training is an active area in statistical machine translation (SMT) (e.g., Och et al., 2002, 2003, Liang et al., 2006, Blunsom et al., 2008, Chiang et al., 2009, Foster et al, 2010, Xiao et al.

5.3 Experiments on the IWSLT2011 benchmark As the second evaluation task, we apply our new method described in this paper to the 2011 IWSLT Chinese-to-English machine translation benchmark (Federico et al., 2011).

Third, the new objective function and new optimization technique are successfully applied to two important machine translation tasks, with implementation issues resolved (e.g., training schedule and hyper-parameter tuning, etc.).

3In machine translation , Huang (2008) provides a soft algorithm that finds the forest oracle, i.e., the parse among the reranked candidates with the highest Parseval F—score.

In machine translation , a decoder that implements forest rescoring (Huang and Chiang, 2007) uses the language model as an external criterion of the goodness of sub-translations on account of their grammaticality.

Discriminative reranking has been employed in many NLP tasks such as syntactic parsing (Char-niak and Johnson, 2005; Huang, 2008), machine translation (Shen et al., 2004; Li and Khudanpur, 2009) and semantic parsing (Ge and Mooney, 2006).

Previous work using topic model for statistical machine translation (SMT) explore topic information at the word level.

To exploit topic information for statistical machine translation (SMT), researchers have proposed various topic-specific lexicon translation models (Zhao and Xing, 2006; Zhao and Xing, 2007; Tam et al., 2007) to improve translation quality.

In addition to the topic-specific lexicon translation method mentioned in the previous sections, researchers also explore topic model for machine translation in other ways.