These input are simplified using our simplification system namely, the DRS-SM model and the phrase-based machine translation system (Section 3.2).

These four sentences are directly sent to the phrase-based machine translation system to produce simplified sentences.

It is useful as a preprocessing step for a variety of NLP systems such as parsers and machine translation systems (Chandrasekar et al., 1996), sum-marisation (Knight and Marcu, 2000), sentence fusion (Filippova and Strube, 2008) and semantic

Machine Translation systems have been adapted to translate complex sentences into $nqfleones(ZhuetaL,2010;VVubbenetaL,2012; Coster and Kauchak, 2011).

To account for deletions, reordering and substitution, Coster and Kauchak (2011) trained a phrase based machine translation system on the PWKP corpus while modifying the word alignment output by GIZA++ in Moses to allow for null phrasal alignments.

(2012) use Moses and the PWKP data to train a phrase based machine translation system augmented with a post-hoc reranking procedure designed to rank the output based on their dissimilarity from the source.

Second, the simplified sentence(s) s’ is further simplified to s using a phrase based machine translation system (PBMT+LM).

The DRS associated with the final M-node D fin is then mapped to a simplified sentence s’fm which is further simplified using the phrase-based machine translation system to produce the final simplified sentence ssimple.

Instead of tasking translators to post-edit the output of machine translation systems , a more interactive approach may be more fruitful.

The standard approach to this problem uses the search graph of the machine translation system .

In the project’s first field trialz, professional translators corrected machine translations of news stories from a competitive English—Spanish machine translation system (Koehn and Haddow, 2012).

When the machine translation system decides for college over university, but the user types the letter u, it should change its prediction.

This paper explores a simple and effective unified framework for incorporating soft linguistic reordering constraints into a hierarchical phrase-based translation system : l) a syntactic reordering model that explores reorderings for context free grammar rules; and 2) a semantic reordering model that focuses on the reordering of predicate-argument structures.

Experiments on Chinese-English translation show that the reordering approach can significantly improve a state-of-the-art hierarchical phrase-based translation system .

Experiments on Chinese-English translation show that the reordering approach can significantly improve a state-of-the-art hierarchical phrase-based translation system .

Then we evaluate the automatic reordering outputs generated from both our translation systems and maximum entropy classifiers.

Parser training includes GEOQUERY test data in order to be less dependent on parse and execution failures in the evaluation: If a translation system , response-based or reference-based, translates the German input into the gold standard English query it should be rewarded by positive task feedback.

We report BLEU (Papineni et al., 2001) of translation system output measured against the original English queries.

Furthermore, we report precision, recall, and Fl-score for executing semantic parses built from translation system outputs against the GEOQUERY database.

Interactive scenarios have been used for evaluation purposes of translation systems for nearly 50 years, especially using human reading comprehension testing (Pfafflin, 1965; Fuji, 1999; Jones et al., 2005), and more recently, using face-to-face conversation mediated via machine translation (Sakamoto et al., 2013).

art machine translation system (the syntax-based variant of Joshua) achieves a score of 26.91, which is reported in (Zaidan and Callison-Burch, 2011).

These have focused on an iterative collaboration between monolingual speakers of the two languages, facilitated with a machine translation system .

Although hiring professional translators to create bilingual training data for machine translation systems has been deemed infeasible, Mechanical Turk has provided a low cost way of creating large volumes of translations (Callison-Burch, 2009; Ambati and Vogel, 2010).

(2013) translated 1.5 million words of Levine Arabic and Egyptian Arabic, and showed that a statistical translation system trained on the dialect data outperformed a system trained on 100 times more MSA data.

We use the NiuTrans 2 toolkit which adopts GIZA++ (Och and Ney, 2003) and MERT (Och, 2003) to train and tune the machine translation system .

This tool scores the outputs in several criterions, while the case-insensitive BLEU-4 (Papineni et al., 2002) is used as the evaluation for the machine translation system .

When top 600k sentence pairs are picked out from general-domain corpus to train machine translation systems , the systems perform higher than the General-domain baseline trained on 16 million parallel data.

In this paper we study the use of sentence-level dialect identification in optimizing machine translation system selection when translating mixed dialect input.

We test our approach on Arabic, a prototypical diglossic language; and we optimize the combination of four different machine translation systems .

We use the open-source Moses toolkit (Koehn et al., 2007) to build four Arabic-English phrase-based statistical machine translation systems (SMT).

Most translation systems adopt the features from a translation model, a language model, and sometimes a reordering model.

The two models are integrated into a hierarchical phrase-based translation system to evaluate their effectiveness.

First, we adopted only the tagged-flattened rules in the hierarchical translation system .