Until quite recently , extending Phrase-based Statistical Machine Translation ( PBSMT ) with syntactic structure caused system performance to deteriorate . In this work we show that incorporating lexical syntactic descriptions in the form of supertags can yield significantly better PBSMT systems . We describe a novel PBSMT model that integrates supertags into the target language model and the target side of the translation model . Two kinds of supertags are employed : those from Lexicalized Tree-Adjoining Grammar and Combinatory Categorial Grammar . Despite the differences between these two approaches , the supertaggers give similar improvements . In addition to supertagging , we also explore the utility of a surface global grammaticality measure based on combinatory operators . We perform various experiments on the Arabic to English NIST 2005 test set addressing issues such as sparseness , scalability and the utility of system subcomponents . Our best result ( 0.4688 BLEU ) improves by 6.1 % relative to a state-of-theart PBSMT model , which compares very favourably with the leading systems on the NIST 2005 task . Within the field of Machine Translation , by far the most dominant paradigm is Phrase-based Statistical Machine Translation ( PBSMT ) ( Koehn et al . , 2003 ; Tillmann & Xia , 2003 ) . However , unlike in rule-and example-based MT , it has proven difficult to date to incorporate linguistic , syntactic knowledge in order to improve translation quality . Only quite recently have ( Chiang , 2005 ) and ( Marcu et al . , 2006 ) shown that incorporating some form of syntactic structure could show improvements over a baseline PBSMT system . While ( Chiang , 2005 ) avails of structure which is not linguistically motivated , ( Marcu et al . , 2006 ) employ syntactic structure to enrich the entries in the phrase table . In this paper we explore a novel approach towards extending a standard PBSMT system with syntactic descriptions : we inject lexical descriptions into both the target side of the phrase translation table and the target language model . Crucially , the kind of lexical descriptions that we employ are those that are commonly devised within lexicon-driven approaches to linguistic syntax , e.g. Lexicalized Tree-Adjoining Grammar ( Joshi & Schabes , 1992 ; Bangalore & Joshi , 1999 ) and Combinary Categorial Grammar ( Steedman , 2000 ) . In these linguistic approaches , it is assumed that the grammar consists of a very rich lexicon and a tiny , impoverished 1 set of combinatory operators that assemble lexical entries together into parse-trees . The lexical entries consist of syntactic constructs ( ' supertags ' ) that describe information such as the POS tag of the word , its subcategorization information and the hierarchy of phrase categories that the word projects upwards . In this work we employ the lexical entries but exchange the algebraic combinatory operators with the more robust and efficient supertagging approach : like standard taggers , supertaggers employ probabilities based on local context and can be implemented using finite state technology , e.g. Hidden Markov Models ( Bangalore & Joshi , 1999 ) . There are currently two supertagging approaches available : LTAG-based ( Bangalore & Joshi , 1999 ) and CCG-based ( Clark & Curran , 2004 ) . Both the LTAG ( Chen et al . , 2006 ) and the CCG supertag sets ( Hockenmaier , 2003 ) were acquired from the WSJ section of the Penn-II Treebank using handbuilt extraction rules . Here we test both the LTAG and CCG supertaggers . We interpolate ( log-linearly ) the supertagged components ( language model and phrase table ) with the components of a standard PBSMT system . Our experiments on the Arabic-English NIST 2005 test suite show that each of the supertagged systems significantly improves over the baseline PBSMT system . Interestingly , combining the two taggers together diminishes the benefits of supertagging seen with the individual LTAG and CCG systems . In this paper we discuss these and other empirical issues . The remainder of the paper is organised as follows : in section 2 we discuss the related work on enriching PBSMT with syntactic structure . In section 3 , we describe the baseline PBSMT system which our work extends . In section 4 , we detail our approach . Section 5 describes the experiments carried out , together with the results obtained . Section 6 concludes , and provides avenues for further work . SMT practitioners have on the whole found it difficult to integrate syntax into their systems . In this work , we have presented a novel model of PBSMT which integrates supertags into the target language model and the target side of the translation model . Using LTAG supertags gives the best improvement over a state-of-the-art PBSMT system for a smaller data set , while CCG supertags work best on a large 2 million-sentence pair training set . Adding grammaticality factors based on algebraic compositional operators gives the best result , namely 0.4688 BLEU , or a 6.1 % relative increase over the baseline . This result compares favourably with the best systems on the NIST 2005 Arabic-English task . We expect more work on system integration to improve results still further , and anticipate that similar increases are to be seen for other language pairs .

Challenge: Improving phrase-based statistical machine translation by incorporating linguistic and syntactic knowledge in a linguistically motivated way remains an open challenge.
Approach: They propose to extend phrase-based statistical machine translation models by injecting lexical knowledge via supertags such as Lexicalized Tree-Adjoining Grammer into the target language model.
Outcome: They show that Utilizing lexical knowledge via supertags for phrase-based statistical machine translation models improves over existing systems on Arabic to English translation tasks.