In modern machine translation practice , a statistical phrasal or hierarchical translation system usually relies on a huge set of translation rules extracted from bi-lingual training data . This approach not only results in space and efficiency issues , but also suffers from the sparse data problem . In this paper , we propose to use factorized grammars , an idea widely accepted in the field of linguistic grammar construction , to generalize translation rules , so as to solve these two problems . We designed a method to take advantage of the XTAG English Grammar to facilitate the extraction of factorized rules . We experimented on various setups of low-resource language translation , and showed consistent significant improvement in BLEU over state-ofthe-art string-to-dependency baseline systems with 200 K words of bi-lingual training data . A statistical phrasal ( Koehn et al . , 2003 ; Och and Ney , 2004 ) or hierarchical ( Chiang , 2005 ; Marcu et al . , 2006 ) machine translation system usually relies on a very large set of translation rules extracted from bi-lingual training data with heuristic methods on word alignment results . According to our own experience , we obtain about 200 GB of rules from training data of about 50 M words on each side . This immediately becomes an engineering challenge on space and search efficiency . A common practice to circumvent this problem is to filter the rules based on development sets in the step of rule extraction or before the decoding phrase , instead of building a real distributed system . However , this strategy only works for research systems , for which the segments for translation are always fixed . However , do we really need such a large rule set to represent information from the training data of much smaller size ? Linguists in the grammar construction field already showed us a perfect solution to a similar problem . The answer is to use a factorized grammar . Linguists decompose lexicalized linguistic structures into two parts , ( unlexicalized ) templates and lexical items . Templates are further organized into families . Each family is associated with a set of lexical items which can be used to lexicalize all the templates in this family . For example , the XTAG English Grammar ( XTAG-Group , 2001 ) , a hand-crafted grammar based on the Tree Adjoining Grammar ( TAG ) ( Joshi and Schabes , 1997 ) formalism , is a grammar of this kind , which employs factorization with LTAG e-tree templates and lexical items . Factorized grammars not only relieve the burden on space and search , but also alleviate the sparse data problem , especially for low-resource language translation with few training data . With a factored model , we do not need to observe exact " template -lexical item " occurrences in training . New rules can be generated from template families and lexical items either offline or on the fly , explicitly or implicitly . In fact , the factorization approach has been successfully applied on the morphological level in previous study on MT ( Koehn and Hoang , 2007 ) . In this work , we will go further to investigate factorization of rule structures by exploiting the rich XTAG English Grammar . We evaluate the effect of using factorized translation grammars on various setups of low-resource language translation , since low-resource MT suffers greatly on poor generalization capability of trans-lation rules . With the help of high-level linguistic knowledge for generalization , factorized grammars provide consistent significant improvement in BLEU ( Papineni et al . , 2001 ) over string-todependency baseline systems with 200 K words of bi-lingual training data . This work also closes the gap between compact hand-crafted translation rules and large-scale unorganized automatic rules . This may lead to a more effective and efficient statistical translation model that could better leverage generic linguistic knowledge in MT . In the rest of this paper , we will first provide a short description of our baseline system in Section 2 . Then , we will introduce factorized translation grammars in Section 3 . We will illustrate the use of the XTAG English Grammar to facilitate the extraction of factorized rules in Section 4 . Implementation details are provided in Section 5 . Experimental results are reported in Section 6 . In this paper , we proposed a novel statistical machine translation model using a factorized structurebased translation grammar . This model not only alleviates the sparse data problem but only relieves the burden on space and search , both of which are imminent issues for the popular phrasal and/or hierarchical MT systems . We took low-resource language translation , especially X-to-English translation tasks , for case study . We designed a method to exploit family information in the XTAG English Grammar to facilitate the extraction of factorized rules . We tested the new model on low-resource translation , and the use of factorized models showed significant improvement in BLEU on systems with 200 K words of bi-lingual training data of various language pairs and genres . The factorized translation grammar proposed here shows an interesting way of using richer syntactic resources , with high potential for future research . In future , we will explore various learning methods for better estimation of families , templates and lexical items . The target linguistic knowledge that we used in this paper will provide a nice starting point for unsupervised learning algorithms . We will also try to further exploit the factorized representation with discriminative learning . Features defined on templates and families will have good generalization capability .

Challenge: Existing statistical phrasal or hierarchical machine translation system relies on a large set of translation rules which results in engineering challenges.
Approach: They propose to use factorized grammar from the field of linguistics as more general translation rules from XTAG English Grammar.
Outcome: The proposed method consistently outperforms existing methods in BLEU on various low-resource language translation tasks with less training data.