Reordering model is important for the statistical machine translation ( SMT ) . Current phrase-based SMT technologies are good at capturing local reordering but not global reordering . This paper introduces syntactic knowledge to improve global reordering capability of SMT system . Syntactic knowledge such as boundary words , POS information and dependencies is used to guide phrase reordering . Not only constraints in syntax tree are proposed to avoid the reordering errors , but also the modification of syntax tree is made to strengthen the capability of capturing phrase reordering . Furthermore , the combination of parse trees can compensate for the reordering errors caused by single parse tree . Finally , experimental results show that the performance of our system is superior to that of the state-of-the-art phrase-based SMT system . In the last decade , statistical machine translation ( SMT ) has been widely studied and achieved good translation results . Two kinds of SMT system have been developed , one is phrase-based SMT and the other is syntax-based SMT . In phrase-based SMT systems ( Koehn et al . , 2003 ; Koehn , 2004 ) , foreign sentences are firstly segmented into phrases which consists of adjacent words . Then source phrases are translated into target phrases respectively according to knowledge usually learned from bilingual parallel corpus . Fi-nally the most likely target sentence based on a certain statistical model is inferred by combining and reordering the target phrases with the aid of search algorithm . On the other hand , syntax-based SMT systems ( Liu et al . , 2006 ; Yamada et al . , 2001 ) mainly depend on parse trees to complete the translation of source sentence . As studied in previous SMT projects , language model , translation model and reordering model are the three major components in current SMT systems . Due to the difference between the source and target languages , the order of target phrases in the target sentence may differ from the order of source phrases in the source sentence . To make the translation results be closer to the target language style , a mathematic model based on the statistic theory is constructed to reorder the target phrases . This statistic model is called as reordering model . As shown in Figure 1 , the order of the translations of " 欧元 " and " 的 " is changed . The order of the translation of " 欧元 / 的 " and " 大幅 / 升值 " is altered as well . The former reordering case with the smaller distance is usually referred as local reordering and the latter with the longer distance reordering as global reordering . Phrase-based SMT system can effectively capture the local word reordering information which is common enough to be observed in training data . But it is hard to model global phrase reordering . Although syntactic knowledge used in syntax-based SMT systems can help reorder phrases , the resulting model is usually much more complicated than a phrase-based system . There have been considerable amount of efforts to improve the reordering model in SMT systems , ranging from the fundamental distance-based distortion model ( Och and Ney , 2004 ; Koehn et al . , 2003 ) , flat reordering model ( Wu , 1996 ; Zens et al . , 2004 ; Kumar et al . , 2005 ) , to lexicalized reordering model ( Tillmann , 2004 ; Kumar et al . , 2005 ; Koehn et al . , 2005 ) , hierarchical phrase-based model ( Chiang , 2005 ) , and maximum entropy-based phrase reordering model ( Xiong et al . , 2006 ) . Due to the absence of syntactic knowledge in these systems , the ability to capture global reordering knowledge is not powerful . Although syntax-based SMT systems ( Yamada et al . , 2001 ; Quirk et al . , 2005 ; Liu et al . , 2006 ) are good at modeling global reordering , their performance is subject to parsing errors to a large extent . In this paper , we propose a new method to improve reordering model by introducing syntactic information . Syntactic knowledge such as boundary of sub-trees , part-of-speech ( POS ) and dependency relation is incorporated into the SMT system to strengthen the ability to handle global phrase reordering . Our method is different from previous syntax-based SMT systems in which the translation process was modeled based on specific syntactic structures , either phrase structures or dependency relations . In our system , syntactic knowledge is used just to decide where we should combine adjacent phrases and what their reordering probability is . For example , according to the syntactic information in Figure 1 , the phrase translation combination should take place between " 大幅 " and " 升值 " rather than between " 的 " and " 大幅 " . Moreover , the non-monotone phrase reordering should occur between " 欧元 / 的 " and " 大幅 / 升值 " rather than between " 欧元 / 的 " and " 大幅 " . We train a maxi-mum entropy model , which is able to integrate rich syntactic knowledge , to estimate phrase reordering probabilities . To enhance the performance of phrase reordering model , some modification on the syntax trees are also made to relax the phrase reordering constraints . Additionally , the combination of other kinds of syntax trees is introduced to overcome the deficiency of single parse tree . The experimental results show that the performance of our system is superior to that of the state-of-art phrasebased SMT system . The roadmap of this paper is : Section 2 gives the related work . Section 3 introduces our model . Section 4 explains the generalization of reordering knowledge . The procedures of training and decoding are described in Section 5 and Section 6 respectively . The experimental results are shown in Section 7 . Section 8 concludes the paper . In this paper , syntactic knowledge is introduced to capture global reordering of SMT system . This method can not only inherit the advantage of local reordering ability of standard phrase-based SMT system , but also capture the global reordering as the syntax-based SMT system . The experimental results showed the effectiveness of our method . In the future work , we plan to improve the reordering model by introducing N-best syntax trees and exploiting richer syntactic knowledge .

Challenge: Existing phrase-based statistical machine translation systems work well for local reordering which is commonly in training data however they fail on the global phrasal level.
Approach: They propose to train a maximum entropy model with syntactic knowledge such as boundary words, POS information and dependencies to guide global phrase reordering.
Outcome: The proposed model outperforms the state-of-the-art statistical machine translation model and found that multiple syntax trees compensate for the errors caused by a single tree.