The bilingual SMT system used in our experiments is the state-of-the-art SCFG decoder CDEC (Dyer et al., 2010)5.

We trained the SMT system on the English-German parallel web data provided in the COMMON CRAWL6 (Smith et al., 2013) dataset.

Method 1 is the baseline system, consisting of the CDEC SMT system trained on the COMMON CRAWL data as described above.

Given a manual German translation of the English query as source sentence, the SMT system produces an English target translation.

This avoids the problem of un-reachability of independently generated reference translations by the SMT system .

in which SMT systems can be trained and evaluated.

(2012) propose a setup where an SMT system feeds into cross-language information retrieval, and receives feedback from the performance of translated queries with respect to cross-language retrieval performance.

However, despite offering direct and reliable prediction of translation quality, the cost and lack of reusability has confined task-based evaluations involving humans to testing scenarios, but prevented a use for interactive training of SMT systems as in our work.

Firstly, update rules that require to compute a feature representation for the reference translation are suboptimal in SMT, because often human-generated reference translations cannot be generated by the SMT system .

Such “un-reachable” gold-standard translations need to be replaced by “surrogate” gold-standard translations that are close to the human-generated translations and still lie within the reach of the SMT system .

Experimental results show that our approach is promising for SMT systems to learn a better translation model.

This proves that bilingually induced topic representation with neural network helps the SMT system disambiguate translation candidates.

For example, translation sense disambiguation approaches (Carpuat and Wu, 2005; Carpuat and Wu, 2007) are proposed for phrase-based SMT systems .

Meanwhile, for hierarchical phrase-based or syntax-based SMT systems , there is also much work involving rich contexts to guide rule selection (He et al., 2008; Liu et al., 2008; Marton and Resnik, 2008; Xiong et al., 2009).

Although these methods are effective and proven successful in many SMT systems , they only leverage within-

For the SMT system , the best translation candidate 6 is given by:

Figure 2: Architecture of SMT system with the sense-based translation model.

Figure 2 shows the architecture of the SMT system enhanced with the sense-based translation model.

Our baseline system is a state-of-the-art SMT system which adapts Bracketing Transduction Grammars (Wu, 1997) to phrasal translation and equips itself with a maximum entropy based reordering model (Xiong et al., 2006).

These glosses, used as the sense predictions of their WSD system, are integrated into a word-based SMT system either to substitute for translation candidates of their translation model or to postedit the output of their SMT system .

We integrate the proposed sense-based translation model into a state-of-the-art SMT system and conduct experiments on Chines-to-English translation using large-scale training data.

To optimize SMT system , we tune the parameters on NIST MT06, and report results on three test sets: MT02, MT03 and MT05.2

In contrast, machine translation uses inherently multilingual data: an SMT system must translate a phrase or sentence from a source language to a different target language, so existing applications of topic models (Eidelman et al., 2012) are wilfully ignoring available information on the target side that could aid domain discovery.

Cross-Domain SMT A SMT system is usually trained on documents with the same genre (e.g., sports, business) from a similar style (e.g., newswire, blog-posts).

Domain Adaptation for SMT Training a SMT system using diverse data requires domain adaptation.

order to simulate pass-through behavior of out-of-vocabulary terms in SMT systems , additional features accounting for source and target term identity were added to DK and BM models.

This approach is advantageous if large amounts of in-domain sentence-parallel data are available to train SMT systems , but relevance rankings to train retrieval models are not.

In a direct translation approach (DT), a state-of-the-art SMT system is used to produce a single best translation that is used as search query in the target language.

For example, Google’s CLIR approach combines their state-of-the-art SMT system with their proprietary search engine (Chin et al., 2008).

In the future, we will extend our methods to other translation models, such as the syntax-based model, to study how to further improve the performance of SMT systems .

In Section 3, we present our models and show how to integrate the models into an SMT system .

They added the labels assigned to connectives as an additional input to an SMT system , but their experimental results show that the improvements under the evaluation metric of BLEU were not significant.

To the best of our knowledge, our work is the first attempt to exploit the source functional relationship to generate the target transitional expressions for grammatical cohesion, and we have successfully incorporated the proposed models into an SMT system with significant improvement of BLEU metrics.

Although modern SMT systems have switched to a discriminative log-linear framework, which allows for additional sources as features, it is generally hard to incorporate dependencies beyond a small window of adjacent words, thus making it difficult to use linguistically-rich models.

We believe that the semantic and pragmatic information captured in the form of DTs (i) can help develop discourse-aware SMT systems that produce coherent translations, and (ii) can yield better MT evaluation metrics.

While in this work we focus on the latter, we think that the former is also within reach, and that SMT systems would benefit from preserving the coherence relations in the source language when generating target-language translations.

We approach this problem by augmenting an SMT system built over target segments with features that reflect the desegmented target words.

In this section, we describe our various strategies for desegmenting the SMT system’s output space, along with the features that we add to take advantage of this desegmented view.

Other approaches train an SMT system to predict lemmas instead of surface forms, and then inflect the SMT output as a postprocessing step (Minkov et al., 2007; Clifton and Sarkar, 2011; Fraser et al., 2012; El Kholy and Habash, 2012b).

We used MT08 and EgyDevV3 to tune SMT systems while we divided the remaining sets among classifier training data (5,562 sentences), dev (1,802 sentences) and blind test (1,804 sentences) sets to ensure each of these new sets has a variety of dialects and genres (weblog and newswire).

This MSA-pivoting system uses Salloum and Habash (2013)’s DA-MSA MT system followed by an Arabic-English SMT system which is trained on both corpora augmented with the DA-English where the DA side is preprocessed with the same DA-MSA MT system then tokenized with MADA-ARZ.

Test sets are similarly preprocessed before decoding with the SMT system .

In the translation tasks, we used the Moses phrase-based SMT systems (Koehn et al., 2007).

In addition, for a detailed comparison, we evaluated the SMT system where the IBM Model 4 was trained from all the training data (I BM 4a“).

Consequently, the SMT system using RN Nu+c trained from a small part of training data can achieve comparable performance to that using I BM 4 trained from all training data, which is shown in Table 3.