Abstract:
A system and method is provided for generating a translingual parsing model and for using the model to machine translate a source language to a destination language. The system and method includes receiving a sentence in the source language, and with the translingual parsing model, searching among and statistically ranking candidate parses each having elements labeled with destination language words, syntactic labels, and role labels indicating relationships between the elements. A statistically high ranked parse is selected and rearranged using the syntactic and role labels, in accordance with word order conventions of the destination language to generate a translingual parse of the source language sentence.

Description:
RELATED APPLICATION 
     This application claims the benefit of U.S. Provisional Application Ser. No. 60/790,076, entitled Provisional Application for Method and System of Machine Translation Using Robust Syntactic Projection and LPCFG Parsing, filed on Apr. 7, 2006, the contents of which are incorporated herein by reference in their entirety for all purposes. 
    
    
     BACKGROUND 
     1. Technical Field 
     This invention relates to automated machine translation, and more particularly to a method and system for machine translating text between source and destination languages. 
     2. Background Information 
     Throughout this application, various publications, patents and published patent applications are referred to by an identifying citation. The disclosures of the publications, patents and published patent applications referenced in this application are hereby incorporated by reference into the present disclosure. 
     Transfer-based translation systems have existed for more than 25 years as exemplified in Bennett and Slocum, 1985, (“The LRC Machine Translation System,” Computational Linguistics, Volume 11, Numbers 2-3, April-September”). Such systems perform translation in three steps. First, the source language text is parsed to determine its syntactic structure. Second, the parse tree is rearranged based on a set of syntactic rules to match the natural order of the destination language. Third, the individual source language words are translated into destination language words. The resultant word sequence is a destination language translation of the source language sentence. A limitation of systems such as this is that all of the knowledge is hand-coded in a complex set of rules and dictionaries, requiring considerable time and effort by computational linguists for each language pair. Furthermore, the rules may interact in unpredictable ways, sometimes preventing any translation from being produced and sometimes producing incorrect translations. It is difficult to control the interactions between rules so that all sentences produce translations and only correct translations are produced. These difficulties are commonly referred to as the problems of “coverage” and “overgeneration.” 
     A statistical approach to translation, referred to as a source-channel model, is described in Brown et al., 1995 (U.S. Pat. No. 5,477,451). (In standard descriptions of source-channel models, the terms source and destination are reversed from their usage in most of this document; “destination language” in the source-channel context refers to the language that is input to the translation device and “source language” is the language produced as output. In keeping with standard terminology, these terms are used in this reversed sense in discussing source-channel models, but this usage is restricted to source-channel models herein.) In this approach, knowledge is acquired automatically from examples of translated sentences, eliminating the need for hand-crafted rules and dictionaries. Furthermore, each possible translation is assigned a probability value. Therefore, there is no need to arrange rules so that only the correct translation is produced; multiple translations may be produced and the translation with e.g., the highest probability score is selected. Nevertheless, such systems have limitations. One limitation is that the channel model—which contains distortion parameters describing how word order differs between source and destination languages—does not to capture basic grammatical regularities. These regularities may be highly informative when transforming grammatical structures between languages (e.g. the transformation of an SVO (subject-verb-object order) language to a VSO (verb-subject-object order) language). A consequence of this limitation is that a large and computationally-expensive search is necessary to determine the ordering of words in the translated sentence. Furthermore, there is little assurance that the selected order is truly grammatical. A second limitation of these approaches involves the source model, which is typically an n-gram language model. Such models account only for local agreement between nearby words and provide no way to determine if entire phrases are grammatical. For those phrases that are indeed grammatical, there is no way to determine if the relationships expressed are plausible. Even if these approaches were combined, the limitations of the source and channel models yield a substantial likelihood of ungrammatical translations or of grammatical translations that are non-sensical. Such behavior may be particularly problematic when downstream automated systems attempt further processing of the translated texts (e.g. to extract a database of facts), since such systems often rely on being able to syntactically parse and analyze their inputs. 
     Yamada and Knight, 2003 (U.S. Patent Application 20030023423) describe a technique that attempts to overcome the aforementioned limitations by introducing an alternative channel model while retaining a source-channel formulation. Like previous work, this formulation includes a probability table that describes how words are translated from the source language to the destination language. In contrast to previous methods, this formulation assumes that the source is a syntactic parse tree rather than a simple sequence of words. A probability table is then introduced to model the possible permutations of tree nodes to account for word-order differences between languages. An additional probability table is introduced to model the insertion of destination language words that have no source language counterpart. A decoder based on this model is described in Yamada and Knight, 2002 (“A decoder for syntax-based MT,” Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics”). This decoder retains an n-gram language model that accounts only for local agreement between nearby words. 
     Other techniques depart entirely from the source-and-channel formulation of the above methods, instead taking the view that “translation is parsing” and defining a single-stage parsing model that describes the entire translation process. Examples of this approach include Wu, 1997 (“Stochastic Inversion Transduction Grammars and Bilingual Parsing of Parallel Corpora,” Computational Linguistics, 23(3):377-403”), Alshawi, 2001 (U.S. Pat. No. 6,233,544), and Melamed, 2004 (“Statistical Machine Translation by Parsing,” Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics”). 
     A common characteristic of these approaches is the use of specialized grammar formalisms that synchronously express sentences in the source and destination languages: in the case of Wu, 1997, “Inversion Transduction Grammars”; in the case of Alshawi, 2001, “Collections of Head Transducers”; and in the case of Melamed, 2004, “Multitext Grammars”. 
     A need exists for a machine translation system and method that addresses the drawbacks of the prior art. 
     SUMMARY 
     In one aspect of the present invention, a method is provided for machine translation from a source language to a destination language. The method includes receiving a sentence in the source language, and with a translingual parsing model, searching among and statistically ranking candidate parses each having elements labeled with destination language words, syntactic labels, and role labels indicating relationships between the elements. The method further includes selecting a statistically high ranked parse for the sentence; and rearranging the parse using the syntactic and role labels, in accordance with word order conventions of the destination language to generate a translingual parse of the source language sentence. 
     In another aspect of the invention, a method is provided for generating a translingual parsing model for machine translating a source language to a destination language. The method includes receiving a destination language treebank having parse trees of destination language sentences, the parse trees having nodes labeled with their syntactic categories. A destination language parsing model is generated using the destination language treebank, and includes parameters for ranking candidate parse trees for a destination language sentence. A second set of destination language sentences is received from a parallel corpus, the parallel corpus including destination language sentences and their respective source language equivalents. The destination language parsing model is applied to the second set of destination language sentences to generate a ranked list of candidate parse trees for each sentence thereof. These candidate parse trees are transformed by applying a rule set associated with linguistic characteristics of the source and destination languages. Linguistic role labels are assigned to nodes of the candidate parse trees, in which the role labels correspond to the linguistic role of a node within its respective parse tree. Grammar constraints are extracted from portions of each candidate parse tree. The translingual parsing model is then estimated using the extracted grammar constraints and source language sentences of the parallel corpus, so that the translingual parsing model includes parameters sufficient to rank candidate parses, wherein the parameters relate elements of the candidate parses including source language words, destination language words, syntactic labels, and role labels. 
     In yet another aspect of the invention, a system is provided for machine translation from a source language to a destination language. The system includes a translation module configured for receiving a sentence in the source language, a translingual parsing model configured for searching among and statistically ranking a plurality of candidate parses. The candidate parses each have elements labeled with destination language words corresponding to words of the source language sentence, syntactic labels indicating a syntactic category of the elements, and role labels indicating relationships between the elements. A parser is configured for selecting the highest statistically ranked parse for the sentence. A reordering module is configured for rearranging at least one of the ranked parses using the syntactic and role labels in accordance with word order conventions of the destination language, to generate a translingual parse of the source language sentence. 
     In still another aspect, a system is provided for generating a translingual parsing model for machine translating a source language to a destination language. This system includes a parser training module configured for receiving a destination language treebank having parse trees of destination language sentences, the parse trees having nodes labeled with their syntactic categories. The parser training module is configured for generating, using the destination language treebank, a destination language parsing model, including parameters for ranking candidate parse trees for a destination language sentence. A destination language parser receives the destination language parsing model and a second set of destination language sentences from a parallel corpus, the parallel corpus including the second plurality of destination language sentences and their respective source language equivalents. The destination language parser is configured for applying the destination language parsing model to the second set of destination language sentences to generate a ranked list of candidate parse trees for each sentence thereof. A tree transformer is configured for transforming the candidate parse trees by applying a rule set associated with linguistic characteristics of the source and destination languages. A role labeler is configured to assign a linguistic role label to nodes of the candidate parse trees, the role label corresponding to the linguistic role of a node within its respective parse tree. An extraction module is configured for extracting grammar constraints from portions of each candidate parse tree. A translingual grammar estimator is configured for estimating the translingual parsing model using the extracted grammar constraints and source language sentences of the parallel corpus, so that the translingual parsing model includes parameters sufficient to rank candidate parses, wherein the parameters relate elements of the candidate parses including source language words, destination language words, syntactic labels, and role labels. 
     In still another aspect, an article of manufacture is provided for machine translating a source language into a destination language. The article of manufacture includes a computer usable medium having a computer readable program code embodied therein. The computer usable medium has computer readable program code for receiving, at a translation module, at least one sentence in the source language. Program code is also provided for searching among and statistically ranking, with a translingual parsing model, a plurality of candidate parses each parse having elements labeled with destination language words corresponding to words of the source language sentence, syntactic labels indicating a syntactic category of the elements, and role labels indicating relationships between the elements. Program code is also provided for selecting the highest statistically ranked parse for the at least one sentence; and rearranging the parse using the syntactic and role labels, in accordance with word order conventions of the destination language to generate a translingual parse of the source language sentence. 
     In still another aspect of the invention, an automated translation machine is provided for translating a source language into a destination language. The translation machine includes means for receiving a sentence in the source language, and means for searching among and statistically ranking candidate parses. The candidate parses each have elements labeled with destination language words corresponding to words of the source language sentence, syntactic labels indicating a syntactic category of the elements, and role labels indicating relationships between the elements. Means are also provided for selecting the highest statistically ranked parse for the sentence, and means for rearranging the parse using the syntactic and role labels in accordance with word order conventions of the destination language to generate a translingual parse of the source language sentence. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The above and other features and advantages of this invention will be more readily apparent from a reading of the following detailed description of various aspects of the invention taken in conjunction with the accompanying drawings, in which: 
         FIG. 1  is a high level functional block diagram of an embodiment of the present invention; 
         FIG. 2  is an exemplary translingual parse tree used by the embodiment of  FIG. 1 ; 
         FIG. 3  is a detailed functional block diagram of a portion of the embodiment of  FIG. 1 ; 
         FIG. 4  is an exemplary destination language parse tree used by the embodiment of  FIG. 1 ; 
         FIGS. 5 to 14  are examples of various rules used by an embodiment of a tree-transformer module of the present invention; 
         FIG. 15  is an example of a transformed tree generated by the tree-transformer of  FIGS. 5 to 14 ; 
         FIGS. 16 to 26  are examples of various rules used by an embodiment of a role-labeler of the present invention; 
         FIG. 27  is an example of a role-labeled tree generated by the role-labeler of  FIGS. 16 to 26 ; 
         FIG. 28  is an example of type-a production data fields used by the embodiments of the present invention; 
         FIG. 29  is an example of a type-a production extractor of the present invention; 
         FIG. 30  is an example of Type-b grammar elements and data fields used by embodiments of the present invention; 
         FIG. 31  is a more detailed functional block diagram of an exemplary translingual grammar estimator of the present invention; and 
         FIG. 32  is a functional block diagram of a Runtime Translation Program portion of the embodiment of  FIG. 1 . 
     
    
    
     DETAILED DESCRIPTION 
     In the following detailed description, reference is made to the accompanying drawings that form a part hereof, and in which is shown by way of illustration, specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention, and it is to be understood that other embodiments may be utilized. It is also to be understood that structural, procedural and system changes may be made without departing from the spirit and scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined by the appended claims and their equivalents. For clarity of exposition, like features shown in the accompanying drawings are indicated with like reference numerals and similar features as shown in alternate embodiments in the drawings are indicated with similar reference numerals. 
     Referring to Figures, embodiments of the present invention are shown. Unlike transfer-based systems such as Bennett and Slocum, 1985, these embodiments automatically learn the required detailed knowledge from data, eliminating the need for a large volume of hand-written rules, e.g., rules that are customized for each particular language. These embodiments consider a wide range of possible translations for each source language sentence and assign a probability to each candidate, thereby overcoming the problems of coverage and overgeneration of conventional rule-based systems discussed above. The relatively few rules used by these embodiments are of a general nature and can be quickly determined by consulting a grammar primer for any particular language of interest. 
     Embodiments of the present invention include a computer-based method and system for translating word sequences in a source language (e.g. Arabic) into word sequences in a destination language (e.g. English). These embodiments are based on statistical algorithms in which parameter values are estimated from at least two data resources: a parallel corpus and a destination language treebank. A large collection of destination language sentences (e.g., a destination language corpus) may optionally be used to further refine the parameter estimates. These embodiments may also use a small amount of general linguistic knowledge contained in various rule sets. 
     As shown in  FIG. 32 , translation is accomplished by parsing a source language sentence to produce a parse tree. As in a typical syntactic parse tree, the resultant parse contains nodes that are labeled with syntactic categories. Unlike a typical parse tree, there is an additional layer of nodes directly dominating the leaves; these pre-terminal nodes are labeled with destination language words. Also unlike a typical parse tree, each node—except the pre-terminal nodes—contains a second label indicating its linguistic relationship (i.e., its ‘role’) relative to its parent node (e.g. SBJ for subject). This type of parse tree is referred to herein as a translingual parse tree (or simply a translingual parse, such as to refer to portions thereof). An example of such a translingual parse tree is shown in  FIG. 2 . 
     Once a translingual parse tree has been obtained for a source language sentence, a simple set of rules is used to rearrange the nodes in accordance with word-order conventions of the destination language. This rearrangement is facilitated by the linguistic-relationship (role) labels in the translingual parse tree. Once the nodes have been rearranged, a second set of rules may be applied to extract the destination language words in the order given by the rearranged tree. The resultant word sequence is a destination language translation of the source language sentence. 
     Alternatively, if the translated output is intended for further automated processing (e.g. by an information extraction system) the rearranged tree—with its source language leaves removed—may be output instead of the word sequence, thereby providing a destination language parse of the source language sentence. 
     Unlike statistical models such as the aforementioned Brown et al., 1995 approach, embodiments of the present invention account for grammatical regularities when transforming word order between languages (e.g. from an SVO language to a VSO language). Furthermore, these embodiments account for long-distance dependencies between head words and their modifiers. These characteristics lead to translations that tend to be more grammatical and semantically coherent, particularly when the source and destination languages are structurally dissimilar. In addition, accounting for grammatical regularities serves to reduce the search space, resulting in a more computational-efficient translation process. 
     Unlike syntax-based channel models such as the aforementioned Yamada and Knight, 2003, embodiments of the present invention are based on a single-stage statistical model that has no separate source and channel components. It requires no probability tables for specifying permutations or word insertions; instead it transforms word order and handles insertions far more efficiently by applying small amounts of general linguistic knowledge. Furthermore, it requires no specialized decoder such as in Yamada and Knight, 2002. Decoding may be performed using a conventional parsing model of the type commonly used for syntactic parsing, e.g., an LPCFG (Lexicalized Probabilistic Context-Free Grammar) similar to that disclosed in Collins, 97, “Three Generative, Lexicalised Models for Statistical Parsing,” Proceedings of the 35th Annual Meeting of the ACL”. Additionally, the lexicalized characteristic of the models eliminates the need for a separate n-gram language model and facilitates long-distance dependencies between words. 
     Unlike the “translation-as-parsing” approaches of Wu, 1997, Alshawi, 2001, and Melamed, 2004, no synchronous grammar is required; the grammar is simply an LPCFG, just as for standard monolingual parsing. There is no need to synchronously account for word order in both languages. Rather, syntactic-relationship information embedded in the translingual parse, together with a small set of general linguistic rules, enables a destination language translation to be synthesized in a natural canonical order. 
     Embodiments of the present invention offer additional advantages over previous methods in estimating its statistical model. Previous syntax-based models assume that the trees obtained by automatically parsing the destination language side of a parallel corpus are correct; in reality, existing parsers make substantial numbers of errors. Previous models further assume that identical linguistic dependencies exist in source language sentences and their destination language translations. In reality, while many linguistic dependencies are shared between source and destination languages, there are substantial numbers of differences. Such assumptions may lead to numerous alignment errors in estimating statistical translation models. Embodiments of the present invention account for parsing inaccuracies and for mismatched dependencies between languages, leading to higher-quality alignments and ultimately to more accurate translations. 
     Data sparseness is often a limiting factor in estimating statistical models, particularly for models involving large numbers of individual words. Previous source-channel formulations partially overcome this limitation by introducing a separate n-gram language model that is separately estimated using a large quantity of monolingual text. However, these models are limited in scope to a few neighboring words. Embodiments of the present invention overcome this limitation by optionally utilizing a large collection of destination language sentences to refine its parsing model. Unlike a separate n-gram source model, the refined estimates of these embodiments are part of the parsing model itself and are thus capable of accounting for dependencies between widely separated words. 
     A characteristic of many of these embodiments is that their parameter values may be estimated using only a parallel corpus and a destination language syntactic treebank such as the University of Pennsylvania Treebank described by Marcus et al., 1994 “Building a Large Annotated Corpus of English: The Penn Treebank,” Computational Linguistics, Volume 19, Number 2”. No treebank is required for the source language. This characteristic may be particularly advantageous for translating some foreign language texts into English, since a treebank is readily available for English but may not be for many other languages. Similarly, as mentioned above, a large destination language corpus may be used to further refine the parameters of the destination language. If the destination language is English, then a virtually unlimited amount of text is available for this purpose. 
     As mentioned hereinabove, if the output of the translation system is intended for further automated processing (e.g. by an information extraction system), then these embodiments are capable of producing a destination language parse of the source language sentence. Thus, the commonly-encountered problem of parsing ungrammatical translations by downstream processing components is reduced or eliminated. 
     Where used in this disclosure, the term “computer” is meant to encompass a workstation, server, personal computer, notebook PC, personal digital assistant (PDA), Pocket PC, smart phone, or any other suitable computing device. 
     The system and method embodying the present invention can be programmed in any suitable language and technology including, but not limited to: C++; Visual Basic; Java; VBScript; Jscript; BCMAscript; DHTM1; XML; CGI; Hypertext Markup Language (HTML), Active ServerPages (ASP); and Javascript. Any suitable database technology can be employed, but not limited to: Microsoft Access and IMB AS 400. 
     Referring now to the Figures, embodiments of the present invention will be more thoroughly described. 
     As shown in  FIG. 1 , an exemplary translation system consists of three top-level components: a translator training program  110 , a translingual parsing model  108 , and a runtime translation program  106 . 
     In particular embodiments, the translator training program receives a destination language treebank  104 , and a parallel corpus  102 , and produces translingual parsing model  108 . The destination language treebank includes parses of destination language sentences annotated with syntactic labels indicating the syntactic category of elements of the parse. An example of a suitable destination language Treebank is the aforementioned University of Pennsylvania Treebank (Marcus et al., 1994). Any number of syntactic labels known to those skilled in the art may be used in embodiments of the present invention. Examples of suitable syntactic labels include those used with the University of Pennsylvania Treebank, such as shown in the following Tables 1 and 2. 
     
       
         
               
             
               
               
               
             
           
               
                 TABLE 1 
               
               
                   
               
               
                 The Penn Treebank POS tagset 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 1. 
                 CC 
                 Coordinating conjunction 
               
               
                 2. 
                 CD 
                 Cardinal number 
               
               
                 3. 
                 DT 
                 Determiner 
               
               
                 4. 
                 EX 
                 Existential there 
               
               
                 5. 
                 FW 
                 Foreign word 
               
               
                 6. 
                 IN 
                 Preposition/subord. 
               
               
                   
                   
                 conjunction 
               
               
                 7. 
                 JJ 
                 Adjective 
               
               
                 8. 
                 JJR 
                 Adjective, comparative 
               
               
                 9. 
                 JJS 
                 Adjective, superlative 
               
               
                 10. 
                 LS 
                 List item marker 
               
               
                 11. 
                 MD 
                 Modal 
               
               
                 12. 
                 NN 
                 Noun, singular or mass 
               
               
                 13. 
                 NNS 
                 Noun, plural 
               
               
                 14. 
                 NNP 
                 Proper noun, singular 
               
               
                 15. 
                 NNPS 
                 Proper noun, plural 
               
               
                 16. 
                 PDT 
                 Predeterminer 
               
               
                 17. 
                 POS 
                 Possessive ending 
               
               
                 18. 
                 PRP 
                 Personal pronoun 
               
               
                 19. 
                 PP$ 
                 Possessive pronoun 
               
               
                 20. 
                 RB 
                 Adverb 
               
               
                 21. 
                 RBR 
                 Adverb, comparative 
               
               
                 22. 
                 RBS 
                 Adverb, superlative 
               
               
                 23. 
                 RP 
                 Particle 
               
               
                 24. 
                 SYM 
                 Symbol 
               
               
                 25. 
                 TO 
                 to 
               
               
                 26. 
                 UH 
                 Interjection 
               
               
                 27. 
                 VB 
                 Verb, base form 
               
               
                 28. 
                 VBD 
                 Verb, past tense 
               
               
                 29. 
                 VBG 
                 Verb, gerund/present participle 
               
               
                 30. 
                 VBN 
                 Verb, past participle 218z 
               
               
                 31. 
                 VBP 
                 Verb, non-3rd ps. sing. present 
               
               
                 32. 
                 VBZ 
                 Verb, 3rd ps. sing. present 
               
               
                 33. 
                 WDT 
                 wh-determiner 
               
               
                 34. 
                 WP 
                 wh-pronoun 
               
               
                 35. 
                 WP$ 
                 Possessive wh-pronoun 
               
               
                 36. 
                 WRB 
                 wh-adverb 
               
               
                 37. 
                 # 
                 Pound sign 
               
               
                 38. 
                 $ 
                 Dollar sign 
               
               
                 39. 
                 . 
                 Sentence-final punctuation 
               
               
                 40. 
                 , 
                 Comma 
               
               
                 41. 
                 : 
                 Colon, semi-colon 
               
               
                 42. 
                 ( 
                 Left bracket character 
               
               
                 43. 
                 ) 
                 Right bracket character 
               
               
                 44. 
                 ″ 
                 Straight double quote 
               
               
                 45. 
                 ‘ 
                 Left open single quote 
               
               
                 46. 
                 “ 
                 Left open double quote 
               
               
                 47. 
                 ’ 
                 Right close single quote 
               
               
                 48. 
                 ” 
                 Right close double quote 
               
               
                   
               
             
          
         
       
     
     
       
         
               
             
               
               
               
             
           
               
                 TABLE 2 
               
               
                   
               
               
                 The Penn Treebank syntactic tagset 
               
               
                   
               
             
             
               
                   
               
             
          
           
               
                 1. 
                 ADJP 
                 Adjective phrase 
               
               
                 2. 
                 ADVP 
                 Adverb phrase 
               
               
                 3. 
                 NP 
                 Noun phrase 
               
               
                 4. 
                 PP 
                 Prepositional phrase 
               
               
                 5. 
                 S 
                 Simple declarative clause 
               
               
                 6. 
                 SBAR 
                 Clause introduced by subordinating conjunction or 0 (see 
               
               
                   
                   
                 below) 
               
               
                 7. 
                 SBARQ 
                 Direct question introduced by wh-word or wh-phrase 
               
               
                 8. 
                 SINV 
                 Declarative sentence with subject-aux inversion 
               
               
                 9. 
                 SQ 
                 Subconstituent of SBARQ excluding wh-word or 
               
               
                   
                   
                 wh-phrase 
               
               
                 10. 
                 VP 
                 Verb phrase 
               
               
                 11. 
                 WHADVP 
                 Wh-adverb phrase 
               
               
                 12. 
                 WHNP 
                 Wh-noun phrase 
               
               
                 13. 
                 WHPP 
                 Wh-prepositional phrase 
               
               
                 14. 
                 X 
                 Constituent of unknown or uncertain category 
               
               
                   
               
             
          
         
       
     
     The parallel corpus  102  includes sentence-aligned pairs of source language sentences and destination language translations. In addition, a large destination language corpus  116  may optionally be received by the training program to further refine the translingual parsing model. The large destination language corpus, if used, includes a collection of unannotated destination language sentences. 
     The runtime translation program  106  receives the translingual parsing model  108  and a collection of source language sentences  100 , and produces destination language translations  112 . Optionally, the runtime translation program produces destination language parse trees  114 . The destination language parse trees  114 , include syntactic parses of the destination language translations, such as shown in  FIG. 2 . In addition, or as an alternative, the source language words may be removed from the parse tree  114  to leave a destination language parse that may be used, e.g., for further processing as discussed in greater detail hereinbelow. 
     Translator Training Program 
     Turning now to  FIG. 3 , the translator training program  110  is described in detail. 
     As shown, in particular embodiments, three components provide a mechanism for syntactically parsing destination language sentences: a parser training program  120 , a destination language parsing model  122 , and a destination language parser  124 . This combination of components implements a LPCFG (lexicalized probabilistic context-free grammar) parser. A LPCFG parser such as disclosed in Collins 1997 may be used for this purpose. Although not required, in particular embodiments, the parser produces a ranked n-best list of possible parses, rather than only the single highest scoring parse. Suitable techniques for producing n-best lists of parse trees are well known, for example as disclosed by Charniak and Johnson, 2005, “Coarse-to-fine n-best parsing and MaxEnt discriminative reranking,” Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics”. 
     Destination language sentences from the parallel corpus  102  arrive at the destination language parser  124 . Applying the destination language parsing model  122 , the destination language parser produces a list of candidate parses for each destination language sentence. This list includes the highest scoring parse, as well as all other parses that have reasonably high scores (e.g. within 50% of the highest scoring parse). 
     The list of candidate parses is received by a tree transformer  126 , which applies simple, general, linguistic rules to transform each tree into a form that is closer the source language. (While the linguistic rules are simple and general, they are language specific and different rules are needed for each language pair. In the examples provided herein, the source language is Arabic and the destination language is English.) These transformations do not account for differences in word order, but rather account for differences in 1) morphology and 2) phrase structure. The rules are not strictly deterministic: if a source language construct is expressible using one of several alternative destination language constructs, the tree transformer  126  produces alternative outputs for each possibility. Therefore, for each tree received as input, the tree transformer  126  will typically produce several alternative transformed trees as output. The following Table 3 lists a set of 10 rules used in the prototype (exemplary) Arabic-to-English system. 
     
       
         
               
               
             
           
               
                 TABLE 3 
               
               
                   
               
               
                 Rule # 
                 Rule 
               
               
                   
               
             
             
               
                 1001 
                 If an NP begins with an article (a, the) or a possessive pronoun  
               
               
                   
                 (his, her, . . .), attach* the article/pronoun to the head noun 
               
               
                 1002 
                 If a phrase includes a conjunction (and), attach the conjunction to  
               
               
                   
                 the head word of the following node 
               
               
                 1003 
                 If a VP consists of an infinitive (to), an auxiliary, or a modal,  
               
               
                   
                 followed by a nested VP, flatten the verb group and attach the 
               
               
                   
                 infinitive/auxiliary/modal to the main verb 
               
               
                 1004 
                 If a VP is headed by a copular verb (is, are, . . .) and followed by  
               
               
                   
                 an adjectival, nominal, or sentential predicate (ADJP, NP, S, . . .),  
               
               
                   
                 attach the copula to the head word of the following predicate 
               
               
                 1005 
                 If an NP includes a nested core NP, flatten the NP to a single  
               
               
                   
                 level 
               
               
                 1006 
                 If an S is headed by a VP, eliminate the VP layer, collapsing its  
               
               
                   
                 contents into the S. 
               
               
                 1007 
                 If a PP contains a preposition followed by an NP, then create two 
               
               
                   
                 alternatives. In alternative 1, leave the prepositional phrase  
               
               
                   
                 unaltered. In alternative 2, attach the preposition to the head word  
               
               
                   
                 of the NP. 
               
               
                 1008 
                 For all possessive NPs, insert an additional NP containing all  
               
               
                   
                 nodes preceding the apostrophe. Create two alternatives. In  
               
               
                   
                 alternative 1, perform no further transformation. In alternative 2,  
               
               
                   
                 attach the apostrophe to the head word of the inserted NP. 
               
               
                 1009 
                 If the subject of an S is a pronoun, then create two alternatives. In 
               
               
                   
                 alternative 1, retain the separate pronoun. In alternative 2, attach  
               
               
                   
                 the pronoun to the head verb. 
               
               
                 1010 
                 If the object of an S is a personal pronoun (him, her, . . .), attach  
               
               
                   
                 the pronoun to the head verb. 
               
               
                   
               
               
                 *Wherever two words are attached, their POS tags are also attached 
               
             
          
         
       
     
       FIGS. 5 through 14  illustrate the operation of each rule by example. The overall operation of the tree transformer is illustrated in  FIGS. 4 and 15 . For example, given the parse tree shown in  FIG. 4 , the tree transformer  126  produces several alternative output trees, one of which is shown in  FIG. 15 . 
     Referring back to  FIG. 3 , a role labeler  128  receives a list of transformed trees from the tree transformer  126 . The role labeler assigns a linguistic role (e.g. SBJ, OBJ) to each constituent in each received tree. Examples of linguistic roles used in the prototype system are given in Table 4. 
     
       
         
               
               
               
             
           
               
                 TABLE 4 
               
               
                   
               
               
                   
                 Role Label 
                 Description 
               
               
                   
               
             
             
               
                   
                 HEAD 
                 Semantic head word of a phrase or clause 
               
               
                   
                 SBJ 
                 Subject of a sentence 
               
               
                   
                 OBJ 
                 Object of a sentence 
               
               
                   
                 SYNHEAD 
                 Syntactic head of a phrase or clause. Used 
               
               
                   
                   
                 only when the syntactic head is different 
               
               
                   
                   
                 than the semantic head, e.g. in a 
               
               
                   
                   
                 prepositional phrase. 
               
               
                   
                 PPMOD 
                 Prepositional phrase modifier 
               
               
                   
                 SMOD 
                 Sentential modifier, e.g. a relative 
               
               
                   
                   
                 clause. 
               
               
                   
                 PREMOD 
                 Any node occurring before the head not 
               
               
                   
                   
                 covered by one of the above labels. 
               
               
                   
                 POSTMOD 
                 Any node occurring after the head not 
               
               
                   
                   
                 covered by one of the above labels. 
               
               
                   
                 MIDMOD 
                 A node that occurs in a sentence or clause 
               
               
                   
                   
                 between the first auxiliary and the head 
               
               
                   
                   
                 node, e.g. an adverb. 
               
               
                   
               
             
          
         
       
     
       FIGS. 15 and 27  provide an example of the operation of the role labeler. Given the transformed tree shown in  FIG. 15  as input, the role labeler  128  produces the tree shown in  FIG. 27  as output. 
     Operation of the role labeler  128  begins by noting the head constituent as determined (predicted) by the destination language parser  124 . (LPCFG parsers such as Collins 97 are head-centric and implicitly identify a head for each constituent. Here, the term head is used in the standard linguistic sense, intuitively the most important word of a phrase or clause. One skilled in the art will recognize in view of this disclosure, that this head information can be retained simply by augmenting the parse tree to make reference thereto, or by storing it in an auxiliary data structure.) A set of rules is then applied by role labeler  128  to determine the linguistic role of each constituent. The following Table 5 lists an exemplary set of 11 rules used in the prototype Arabic-to-English system. 
     
       
         
               
               
               
             
           
               
                 TABLE 5 
               
               
                   
               
               
                   
                 Applies to 
                   
               
               
                 Rule # 
                 category 
                 Rule 
               
               
                   
               
             
             
               
                 2001 
                 all 
                 Assign the role HEAD to all nodes predicted as heads  
               
               
                   
                   
                 by the parser. 
               
               
                 2002 
                 S 
                 Find the rightmost non-temporal* NP preceding the  
               
               
                   
                   
                 head (if any). Label it with role SBJ. 
               
               
                 2003 
                 S 
                 Find the leftmost non-temporal NP following the  
               
               
                   
                   
                 head (if any). Label it with role OBJ. 
               
               
                 2004 
                 PP 
                 If the PP contains a preposition followed by an NP or  
               
               
                   
                   
                 S, label the preposition as SYNHEAD and the NP/S  
               
               
                   
                   
                 as HEAD (overrides rule 2001). 
               
               
                 2005 
                 SBAR 
                 If the SBAR contains a complementizer followed by  
               
               
                   
                   
                 an S, label the complementizer as SYNHEAD and the  
               
               
                   
                   
                 S as HEAD (overrides rule 2001). 
               
               
                 2006 
                 NP 
                 If the NP is possessive, label the ‘s (or ‘) as  
               
               
                   
                   
                 SYNHEAD and the preceding NP as HEAD  
               
               
                   
                   
                 (overrides rule 2001). 
               
               
                 2007 
                 all 
                 Assign role PPMOD to all PPs. 
               
               
                 2008 
                 all 
                 Assign role SMOD to all SBARs. 
               
               
                 2009 
                 S 
                 For any nodes occurring between the first auxiliary  
               
               
                   
                   
                 and the head verb (in the untransformed parse) and  
               
               
                   
                   
                 not covered by one of the above rules, assign the role  
               
               
                   
                   
                 MIDMOD 
               
               
                 2010 
                 all 
                 For any nodes preceding the head node and not  
               
               
                   
                   
                 covered by one of the above rules, assign role  
               
               
                   
                   
                 PREMOD 
               
               
                 2011 
                 all 
                 For any nodes following the head node and not  
               
               
                   
                   
                 covered by one of the above rules, assign role  
               
               
                   
                   
                 POSTMOD 
               
               
                   
               
               
                 *Temporal NPs are those headed by temporal words (e.g. Monday, morning, yesterday, week, etc.) 
               
             
          
         
       
     
       FIGS. 16 through 26  illustrate the operation of each rule by example. 
     A type-a productions extractor  130  receives a list of role-labeled trees from the role labeler  128 . Such lists are potentially large. For example, for a single input sentence, the destination language parser  124  may produce a list of 50 plausible parse trees. If the tree transformer  126  then produced an average of 10 transformed trees for each possible parse, the type-a productions extractor  130  would receive a list of 500 role-labeled trees. 
     The purpose of the type-a productions extractor  130  is to extract the non-terminal productions from each of the role-labeled trees associated with a particular sentence of parallel corpus  102 , and to construct a single set of productions by taking the union of productions from this entire list of received trees. This set of productions defines a compact grammar that can generate the entire list of received trees. (Since most productions are shared among multiple trees in the n-best list, the number of productions increases far less rapidly than the number of trees.)  FIG. 28  shows an exemplary data structure used to represent the productions. (These productions are referred to as ‘type-a productions’ to distinguish them from other types of productions that will be discussed hereinbelow.) 
     These type-a productions are lexicalized, just as in an LPCFG as described hereinabove. Furthermore, unit productions, i.e. productions where the right-hand-side (RHS) contains only a single element, are folded into their parent&#39;s production. Specifically, each RHS element contains a path that lists the intermediate nodes between the parent node and the next branching or pre-terminal node. (The nodes below the topmost node of a path can have only one possible role: HEAD. Therefore there is no need to explicitly represent role information for elements of paths.) Therefore, except for top productions, (top productions have an LHS category of TOP and a single RHS element that is the topmost node in a tree) there are no type-a unit productions.  FIG. 29  shows an example tree and the set of type-a productions extracted from it. Note the unit production path in element  1  of production  142 . One skilled in the art will recognize that the type-a productions can be extracted from a parse tree in the same way that productions of standard context-free grammar can be extracted from any typical parse tree. 
     Referring back to  FIG. 3 , a translingual grammar estimator  132  receives sets of type-a productions ( 168 ,  FIG. 31 ) from the type-a productions extractor  130 , each set being derived from a single destination language sentence, i.e., from parallel corpus  102 . Additionally, the translingual grammar estimator  132  receives a source language translation ( 170 ,  FIG. 31 ) of each destination language sentence from the parallel corpus  102  as shown. From these two input sources, the translingual grammar estimator  132  generates a translingual parsing model  108 . The model  108  thus includes parameters sufficient to rank candidate parses, the parameters relating elements of the candidate parses including source language words, destination language words, syntactic labels, and role labels. 
     As mentioned hereinabove, the translingual grammar estimator  132  may optionally receive a large destination language corpus  116  to further refine the translingual parsing model  108 . An example of estimator  132  is discussed in detail hereinbelow. 
     Operation of translingual grammar estimator  132 , e.g., to produce the translingual grammar, is treated as a constrained grammar induction exercise. Specifically, estimator  132  is configured to induce a LPCFG grammar (referred to herein as a type-b grammar) that is capable of generating the source language training sentences, subject to sets of constraints that are derived from their destination language counterparts. This grammar induction may be effected using the well-known inside-outside algorithm described by Baker, 79, (“Trainable grammars for speech recognition,” Proceedings of the Spring Conference of the Acoustical Society of America, pages 547-550”). 
     In particular embodiments, specific constraints imposed on the induced type-b grammar include:
         If a received type-a production contains modifiers m 1 , m 2 , . . . , m n  that are attached to head h with roles r 1 , r 2 , . . . , r n  respectively, then the induced grammar permits the same set of attachments using the same roles, but does not require that the modifiers be attached in the same order as in the type-a production. Instead, any ordering of the modifiers is permitted.   For any particular derivation in the induced grammar, a bounded number of attachments are permitted that occur in none of the received type-a productions. That is, a modifier m may attach to head h, even if there is no type-a production in which m attaches to h. In such cases, the role assigned to the attachment is “inserted”. Such attachments facilitate grammar induction in cases where mismatches occur between source and destination language dependencies. To keep the estimation procedure tractable, only one such inserted attachment per tree is currently permitted, although more may be provided in some embodiments.   Not all modifiers occurring in a received type-a production are required to be present by the induced grammar; some may be omitted. For example, in a particular type-a production, modifiers m 1 , m 2 , and m 3  may be attached to head h, but the constrained grammar would permit a constituent in which only m 2  and m 3  are attached to head h.   Each modifier is permitted to attach only once within a constituent. For example, the modifier (NP, the_representative) with role SBJ would be permitted to attach only once to head (S, emphasized).       

     To enforce the foregoing constraints, an architecture for grammar estimator  132  may be used, which includes a parser  160  that is decoupled from the type-b grammar  162  as shown in  FIG. 31 . Formally, in particular embodiments, the type-b grammar  162  is a 4-tuple consisting of 1) a set of variables, 2) a set of terminals, 3), a set of probabilistic productions, and 4) a unique TOP variable. To distinguish this from other types of grammars used in the system, this is referred to it as a type-b grammar, and its components as type-b variables, type-b terminals, and type-b productions. 
     Type-b variables are not simple symbols such as may be expected in a conventional grammar, but rather, are composite objects that additionally maintain state information. This state information is used to enforce various types of constraints. For example, each type-b variable contains a “complement” field that tracks the remaining modifiers not yet attached to a constituent, thereby permitting enforcement of the constraint that each modifier can attach only once. A brief explanation of the data fields used in type-b variables is given in the following Table 6. 
     
       
         
               
               
             
           
               
                 TABLE 6 
               
               
                   
               
               
                 Field 
                 Description 
               
               
                   
               
             
             
               
                 Category 
                 A nonterminal symbol such as S, VP, NP, etc. 
               
               
                 Head word 
                 The head word of this constituent (as in a LPCFG). 
               
               
                 Complement  
                 Parser state information: the remaining elements of the  
               
               
                 set 
                 type-a production not yet expressed. 
               
               
                 Left edge 
                 Parser state information: the leftmost modifier attached to  
               
               
                   
                 the constituent so far. Form is (category, role). 
               
               
                 Right edge 
                 Parser state information: the rightmost modifier attached  
               
               
                   
                 to the constituent so far. Form is (category, role). 
               
               
                 A_production 
                 Parser state information: the type-a production from  
               
               
                   
                 which this constituent derives 
               
               
                 Missing  
                 Parser state information: used to account for differences in 
               
               
                 constituent 
                 dependencies between source and destination languages. 
               
               
                   
                 The form is (category, head word). A missing constituent  
               
               
                   
                 is one that the type-a production predicts should be  
               
               
                   
                 attached, but has been omitted. 
               
               
                 Inserted  
                 Parser state information: used to account for differences in 
               
               
                 constituent 
                 dependencies between source and destination languages. 
               
               
                   
                 The form is (category, head word). An inserted constituent  
               
               
                   
                 is one that has been attached although it is not predicted  
               
               
                   
                 by the type-a production. 
               
               
                 Total  
                 Parser state information: total count of mismatched 
               
               
                 mismatches 
                 attachments occurring anywhere in the parse. Maximum is  
               
               
                   
                 1 in the prototype system. 
               
               
                   
               
             
          
         
       
     
     Type-b terminals are contiguous sequences of one or more source language words (in the prototype system, terminals may be up to three words in length). By permitting terminals that are sequences of more than one word, a single destination language term can be aligned to multiple source language words. 
     Type-b productions, as shown in  FIG. 30 , are of three types: top productions (tp-productions), binary-branching productions (br-productions), and leaf productions (lf-productions). During grammar estimation (at estimator  132 ,  FIG. 3 ), the parser  160  requests productions (at  164 ) from the grammar  162  and receives a (possibly empty) list of type-b productions (at  166 ) that satisfy the request in return. An unusual characteristic of the grammar  162  is that its elements are created “on the fly” as needed to satisfy requests from the parser. Thus, the grammar  162  can verify that all constraints are satisfied before returning its response, and can dynamically create variables that reflect the current state of the derivation. 
     In these embodiments, for each request, the parser  160  passes a RHS (right hand side) to the grammar  162  and receives a list of productions  166  that have the specified RHS in return. This arrangement supports various bottom-up parsing strategies, such as the well-known CKY algorithm as well as the inside-outside algorithm. Three types of requests are supported:
         Request_top_productions (B_Variable vr)—returns a top production (if any exists) where TOP-&gt;vr.   Request_branching_productions (B_Variable vr 1 , B_Variable vr 2 )—returns a list of branching productions (if any) where vr 1  attaches to vr 2  or where vr 2  attaches to vr 1 .   Request_leaf_productions (B_Terminal tr)—returns a list of leaf productions that produce the source language word(s) tr.       

     Appendix A gives illustrative pseudo-code describing the implementation of each of these request types. 
     In particular embodiments, translingual grammar estimator  132  operates in three phases. During phase 1, the inside-outside algorithm is used to estimate an approximate probability model for LF_Productions only (i.e. the translation probabilities for words). These approximate probabilities allow for greater pruning during the subsequent estimation phases. If desired, the probabilities for LF_Productions may be initialized prior to phase 1 with estimates taken from a finite-state model, such as Model 1 described by Brown et al., 1995. Such initialization tends to help speed convergence. During phase 2, the inside-outside algorithm is used to determine probabilities for all three b_production types: TP_Productions, BR_Productions, as well as LF_Productions. (In some embodiments, phase 2 may be omitted entirely at a cost of some potential degradation in model accuracy.) For both phase 1 and phase 2, several iterations of inside-outside estimation ( 172 ,  FIG. 31 ) are typically used (e.g., at least 2 or 3). During phase 3, a conventional probabilistic CKY parsing algorithm may be used—optionally with model parameters from phase 2—to extract the single highest-scoring parse for each sentence. The result is a treebank of translingual parse trees such as shown in  FIG. 2 . From this treebank, frequencies are extracted for computing the probabilities of the translingual parsing model  108 . (In principle, it is not strictly necessary to construct a treebank of 1-best parses prior to extracting the frequencies; it is possible to extract the frequencies directly from the final inside-outside pass of phase 2. However, constructing a 1-best treebank tends to substantially reduce the size of the final model.) Specific examples of these three phases are now described. 
     Phase-1 Probability Model 
     In representative embodiments, branching probabilities for standard attachments are fixed at 1.0 and thus have no effect in this phase. Branching probabilities for mismatched dependencies are set to a fixed penalty (e.g. 0.05). Top probabilities are also fixed at 1.0. Only the LF_Production probabilities are updated during this phase. A unigram probability model is used for these probabilities. 
     Phase-2 Probability Model 
     In this phase, branching and top probabilities are also updated. Branching productions are factored into three parts: 1) head prediction, 2) modifier prediction, and 3) modifier headword prediction. The following formulas refer to variable prev_category and prev_role. For productions that extend a partially-constructed constituent, prev_category and prev_role are taken from either the left_edge or right_edge of the head variable, depending on branching direction. For productions that start a new constituent, prev_category and prev_role are both nil. 
     Phase-3 Probability Model 
     During phase 3, a translingual treebank is constructed and the translingual parsing model  108  is produced. This model has two parts. 
     The first part is a model that generates one or more source language words, given a destination language term and its category (i.e., syntactic label). The probabilities for this model may be identical to those for LF_Production above. 
     The second part is a LPCFG similar to Model 1 described in Collins, 97, except that:
         Path and role are predicted simultaneously, together with syntactic category and label.   The distance feature is eliminated.   1st order Markov features are added—previous_category and previous_role       

     For, completeness, the details of the model are described below. 
     Head Prediction:
 
P(hc,hp|pc,pt,pw)
 
where
         hc is the head category   hp is the head path   pc is the parent category   pt is the parent part-of-speech tag   pw is the parent word
 
Modifier Prediction:
 
P(mc,mt,mw,mp,mr|pc,pt,pw,hc,pm)
 
where
   mc is the modifier category   mt is the modifier tag   mw is the modifier word   mp is the modifier path   mr is the modifier role   pc, pt, pw, and hc are as above in the head prediction case   pm is the syntactic category of the previous modifier (next closest to head)       

     Modifier prediction is then factored into two parts: 1) prediction of category, tag, path, and role, and 2) prediction of headword. 
     As a practical matter, all probabilities in this model should be smoothed. Smoothing techniques are well-known in the literature, for example, see Collins, 97. 
     Optionally Improving the Model with Statistics from a Large Destination Language Corpus 
     As mentioned hereinabove, statistics derived from a large destination language corpus can be added straightforwardly to the model described above. Since modifier prediction is factored into two parts and the part predicting headwords is not dependent on the previous modifier, statistics for modifier-headword prediction are insensitive to modifier order. Therefore, there is no problem incorporating headword statistics from trees where the modifier order is different. In particular, there is no barrier to incorporating modifier-headword statistics from destination language parse trees. 
     As shown in  FIG. 3 , a large destination language corpus  116 —if provided—is parsed by destination language parser  124 . In particular embodiments, only the 1-best parse is used for each sentence. Next, the tree transformer  126 , as described hereinabove, transforms each tree into a form that is closer to the source language. If the rules lead to multiple trees for each parse, then all resulting trees may be used. The role labeler  128  adds role labels to the transformed trees, again as described earlier. Finally, a lexical counts extractor  136  extracts bi-lexical statistics which are similar to the statistics generated by translingual grammar estimator  132  as discussed hereinabove. These statistics are then appended to the (e.g., bi-lexical) statistics extracted from the translingual treebank by estimator  132  produced during phase 3 above. The combined statistics are then used to compute the (bi-lexical) probabilities in the translingual parsing model  108 . 
     Runtime Translation Program  106   
     Turning to  FIG. 32 , once a translingual parsing model  108  has been estimated, translation requires little more than LPCFG parsing. As shown, source language sentences  100  arrive at the translingual parser  180 . The translingual parser also receives the translingual parsing model  108  as described hereinabove. Before parsing proceeds, the parser&#39;s chart is seeded with the possible translations of each contiguous group of one or more source language words, where the possible translations and their probabilities are given by the LF_Productions described above. (Up to 3 words may be used in the exemplary/prototype system.) Thus, the chart is seeded with 1) destination language words, 2) their syntactic and role labels, and 3) the probabilities of those word/label pairs producing the source language words. At this point, parsing proceeds just as for the monolingual case, except that the resulting tree contains role information for each node. 
     A tree reorderer  182  uses that role information to rearrange the tree into a natural word order for the destination language. However, the tree reorderer  182  first applies inverse rules to undo the morphological changes made by the tree transformer  126  (e.g. to separate out determiners from head nouns, break up verb groups into individual words, etc.). Once the morphological changes have been undone, tree rearrangement proceeds. In the prototype system, where the destination language is English, the rearrangement rules are as follows:
         SBJ nodes immediately precede the head verb   OBJ nodes immediately follow the head verb   POSTMOD nodes follow the head node, after the OBJ if present   PPMOD nodes follow the head node, after POSTMODs if any   SMOD nodes follow the head node, after PPMODs if any   MIDMOD nodes are placed after the first verb of the verb group   PREMOD nodes are placed before the head node, preceding the SBJ if present. The order of any PREMOD nodes that occur post-head in the translingual parse tree is reversed before moving them to the pre-head position.   SYNHEAD nodes precede the head node, prior to PREMODs if any   Determiners and possessive pronouns are placed at the front of noun phrases   Prepositions are moved to the front of prepositional phrases       

     Once a source language sentence has been parsed and reordered, the translation process is essentially complete. If a conventional translation is required, an extract pre-terminals process  184  extracts the pre-terminal nodes, yielding the translated sentence. Alternatively, if a destination language parse tree is required, a remove leaves process  186  removes the source language terminals, leaving the destination language parse. 
     It should be understood that any of the features described with respect to one of the embodiments described herein may be used with any other of the embodiments described herein without departing from the spirit and scope of the present invention. 
     It should further be understood that although the foregoing embodiments have been described as software elements running on a computer or other smart device, nominally any of the various modules or processes described herein may be implemented in software, hardware, or any combination thereof, without departing from the spirit and scope of the present invention. 
     In the preceding specification, the invention has been described with reference to specific exemplary embodiments thereof. It will be evident that various modifications and changes may be made thereunto without departing from the broader spirit and scope of the invention as set forth in the claims that follow. The specification and drawings are accordingly to be regarded in an illustrative rather than restrictive sense. 
     
       
         
               
             
           
               
                 APPENDIX A 
               
               
                   
               
             
             
               
                 request_top_productions(B_Variable vr) 
               
               
                  // returns a list of TP_Productions where rhs matches vr 
               
               
                 { 
               
               
                  // return nothing if vr is incomplete or if a mismatched 
               
               
                  // dependency is still unresolved 
               
               
                  if (vr.complement_set ≠ [ ]) or (vr.missing_constituent ≠ NIL) or 
               
               
                   (vr.inserted_constituent ≠ NIL) 
               
               
                  { 
               
               
                   return empty list 
               
               
                  } 
               
               
                  let result = empty list of TP_Productions 
               
               
                  let aprods = all A_Productions for this sentence where the lhs category 
               
               
                        is TOP and vr satisfies the rhs (i.e. vr&#39;s category and 
               
               
                        headword match those specified in the A_Productions 
               
               
                        rhs) 
               
               
                  forall ap in aprods { 
               
               
                   // add a TP_Production to the result that corresponds to ap 
               
               
                   // first create the lhs for the new production 
               
               
                   let vl = new B_Variable 
               
               
                   vl.category = TOP 
               
               
                   vl.headword = vr.headword 
               
               
                   vl.complement_set = [ ] 
               
               
                   vl.left_edge = NIL 
               
               
                   vl.right_edge = NIL 
               
               
                   vl.a_production = ap 
               
               
                   vl.missing_constituent = NIL 
               
               
                   vl.inserted_constituent = NIL 
               
               
                   vl. total_mismatches = 0; 
               
               
                   // now add the new production 
               
               
                   let tp = new TP_Production 
               
               
                   tp.lhs = vl 
               
               
                   tp.rhs.variable = vr 
               
               
                   tp.rhs.path = ap[1].path 
               
               
                   tp.rhs.role = ap[1].role; 
               
               
                   tp.probability = compute_prob(tp) 
               
               
                   append tp to result 
               
               
                  } 
               
               
                  return result 
               
               
                 } 
               
               
                 request_branching_productions(B_Variable vr1, B_Variable vr2) 
               
               
                  // returns a list of BR_Productions where the rhs is (vr1 vr2) 
               
               
                 { 
               
               
                  // check if constraint on mismatched dependencies is violated 
               
               
                  if ((vr1.total_mismatches + vr2.total_mismatches) == 2) { 
               
               
                   // possible violation, check some more 
               
               
                   if ((vr1.missing_constituent) == (vr2.inserted_constituent)) OR 
               
               
                    (vr2.missing_constituent) == (vr1.inserted_constituent)) 
               
               
                   { 
               
               
                    // ok, two mismatches but they&#39;re the same and cancel out 
               
               
                    let missing = NIL // nothing missing since cancelled out 
               
               
                    let inserted = NIL // likewise, nothing inserted 
               
               
                    let mismatches = 1 // really only one since the same 
               
               
                   } else { 
               
               
                    // quit, can&#39;t have more than one mismatch 
               
               
                    return empty list 
               
               
                   } 
               
               
                  } else { 
               
               
                   // no violation, determine number of mismatches so far 
               
               
                   let mismatches = vr1.total_mismatches + vr2.total_mismatches 
               
               
                   let missing = any missing constituent in vr1 or vr2, NIL otherwise 
               
               
                   let inserted = any inserted constituent in vr1 or vr2, NIL otherwise 
               
               
                  } 
               
               
                  // check if vr1 and vr2 are both complete constituents 
               
               
                  if (vr1.complement_set == [ ]) and (vr2.complement_set == [ ]) { 
               
               
                   // start new constituents where vr1 is head and vr2 is modifier 
               
               
                   let aprods1 = all A_Productions for this sentence where the head role 
               
               
                        is satisfied by vr1 and where vr2 satisfies a modifier role 
               
               
                   forall ap in aprods1 { 
               
               
                    // add a BR_Production to the result that corresponds to ap 
               
               
                    let head_idx = index of ap&#39;s rhs that vr1 satisfies as the head 
               
               
                    let mod_idx = index of ap&#39;s rhs that vr2 satisfies as a modifier 
               
               
                    // first create the lhs for the new production 
               
               
                    let vl = new B_Variable 
               
               
                    vl.category = ap.lhs.category 
               
               
                    vl.headword = ap.lhs.headword 
               
               
                    vl.complement_set = ap.rhs − ap.rhs[mod_idx] 
               
               
                    vl.left_edge = NIL 
               
               
                    vl.right_edge = {vr2.category, ap[mod_idx].role} 
               
               
                    vl.a_production = ap 
               
               
                    vl.missing_constituent = missing 
               
               
                    vl.inserted_constituent = inserted 
               
               
                    vl.total_mismatches = mismatches 
               
               
                    // now add the new production 
               
               
                    let br = new BR_Production 
               
               
                    br.lhs = vl 
               
               
                    br.rhs.left_variable = vr1 
               
               
                    br.rhs.left_path = ap[head_idx].path 
               
               
                    br.rhs.left_role = ap[head_idx].role 
               
               
                    br.rhs.right_variable = vr2 
               
               
                    br.rhs.right_path = ap[mod_idx].path 
               
               
                    br.rhs.right_role = ap[mod_idx].role 
               
               
                    br.probability = compute_prob(br) 
               
               
                    append br to result 
               
               
                   } 
               
               
                   // start new constituents where vr2 is head and vr1 is modifier 
               
               
                   let aprods2 = all A_Productions for this sentence where the head role 
               
               
                        is satisfied by vr2 and where vr1 satisfies a modifier role 
               
               
                   forall ap in aprods2 { 
               
               
                    // add a BR_Production to the result that corresponds to ap 
               
               
                    let head_idx = index of ap&#39;s rhs that vr2 satisfies as the head 
               
               
                    let mod_idx = index of ap&#39;s rhs that vr1 satisfies as a modifier 
               
               
                    // first create the lhs for the new production 
               
               
                    let vl = new B_Variable 
               
               
                    vl.category = ap.lhs.category 
               
               
                    vl.headword = ap.lhs.headword 
               
               
                    vl.complement_set = ap.rhs − ap.rhs[mod_idx] 
               
               
                    vl.left_edge = {vr1.category, ap[mod_idx].role} 
               
               
                    vl.right_edge = NIL 
               
               
                    vl.a_production = ap 
               
               
                    vl.missing_constituent = missing 
               
               
                    vl.inserted_constituent = inserted 
               
               
                    vl.total_mismatches = mismatches 
               
               
                    // now add the new production 
               
               
                    let br = new BR_Production 
               
               
                    br.lhs = vl 
               
               
                    br.rhs.left_variable = vr1 
               
               
                    br.rhs.left_path = ap[mod_idx].path 
               
               
                    br.rhs.left_role = ap[mod_idx].role 
               
               
                    br.rhs.right_variable = vr2 
               
               
                    br.rhs.right_path = ap[head_idx].path 
               
               
                    br.rhs.right_role = ap[head_idx].role 
               
               
                    br.probability = compute_prob(br) 
               
               
                    append br to result 
               
               
                   } 
               
               
                  } 
               
               
                  // check if vr1 is partially constructed and vr2 is complete 
               
               
                  if (vr1.complement_set ≠ [ ]) and (vr2.complement_set == [ ]) { 
               
               
                   // attempt to extend vr1 constituent by attaching vr2 
               
               
                   if (an element of vr1&#39;s complement_set is satisfied by vr2) { 
               
               
                    let idx = index into vr1&#39;s complement_set of element satisfied vr2 
               
               
                    // add a BR_Production where vr2 extends vr1 
               
               
                    // first create the lhs for the new production 
               
               
                    let vl = copy of vr1 
               
               
                    vl.complement_set = vr1.complement_set − 
               
               
                    vr1.complement_set[idx] 
               
               
                    vl.right_edge = {vr2.category, vr1.complement_set[idx].role} 
               
               
                    // now add the new production 
               
               
                    let br = new BR_Production 
               
               
                    br.lhs = vl 
               
               
                    br.rhs.left_variable = vr1 
               
               
                    br.rhs.left_path = NIL 
               
               
                    br.rhs.left_role = HEAD 
               
               
                    br.rhs.right_variable = vr2 
               
               
                    br.rhs.right_path = vr1.complement_set[idx].path 
               
               
                    br.rhs.right_role = vr1.complement_set[idx].role 
               
               
                    br.probability = compute_prob(br) 
               
               
                    append br to result 
               
               
                   } 
               
               
                  // check if vr2 is partially constructed and vr1 is complete 
               
               
                  if (vr2.complement_set ≠ [ ]) and (vr1.complement_set == [ ]) { 
               
               
                   // attempt to extend vr2 constituent by attaching vr1 
               
               
                   if (an element of vr2&#39;s complement_set is satisfied by vr1) { 
               
               
                    let idx = index into vr2&#39;s complement_set of element satisfied vr1 
               
               
                    // add a BR_Production where vr1 extends vr2 
               
               
                    // first create the lhs for the new production 
               
               
                    let vl = copy of vr2 
               
               
                    vl.complement_set = vr2.complement_set − 
               
               
                    vr2.complement_set[idx] 
               
               
                    vl.left_edge = {vr1.category, vr2.complement_set[idx].role} 
               
               
                    // now add the new production 
               
               
                    let br = new BR_Production 
               
               
                    br.lhs = vl 
               
               
                    br.rhs.left_variable = vr1 
               
               
                    br.rhs.left_path = vr2.complement_set[idx].path 
               
               
                    br.rhs.left_role = vr2.complement_set[idx].role 
               
               
                    br.rhs.right_variable = vr2 
               
               
                    br.rhs.right_path = NIL 
               
               
                    br.rhs.right_role = HEAD 
               
               
                    br.probability = compute_prob(br) 
               
               
                    append br to result 
               
               
                   } 
               
               
                  } 
               
               
                  // now create complete versions of partially complete constituents. That 
               
               
                  // is, create complete versions where one or more elements from the 
               
               
                  // original A_Production are missing 
               
               
                  let incs = all productions from result list where complement_set ≠ [ ] 
               
               
                  forall br1 in incs { 
               
               
                   let br2 = copy of br1 
               
               
                   let vl2 = copy of br1.lhs 
               
               
                   // update probability score with deletion penalties 
               
               
                   br2.probability = br1.probability * 
               
               
                   completion_score(vl2.complement_set) 
               
               
                   vl2.complement_set = [ ] 
               
               
                   br2.lhs = vl2 
               
               
                   append br2 to result 
               
               
                  } 
               
               
                  // now attach mismatched constituents 
               
               
                  if (mismatched_attachments_permitted) { 
               
               
                   // attempt to extend vr1 by attaching vr2 
               
               
                   // make sure not attaching to a part-of-speech tag 
               
               
                   if (vr1.category is not a part-of-speech tag) { // e.g. as nn or dt_nn 
               
               
                    // make sure vr2 is a completed constituent 
               
               
                    if (vr2.complement_set == [ ]) { 
               
               
                     // make sure no more than 1 mismatched dependency 
               
               
                     if ((vr1.total_mismatches + vr2.total_mismatches) == 0) OR 
               
               
                      (vr1.missing_constituent is satisfied by vr2) // vr1 is missing 
               
               
                      vr2 
               
               
                     { 
               
               
                      // finally, make sure this really is a mismatched attachment 
               
               
                      if (NOT (vr2 satisfies some role in vr1.a_production.rhs)) { 
               
               
                       let vl = copy of vr1 
               
               
                      if (vr1.missing_constituent == NIL) { 
               
               
                       set vl.inserted_constituent with fields from vr2 
               
               
                      } else { 
               
               
                       vl.missing_constituent = NIL 
               
               
                      } 
               
               
                      vl.total_mismatches = 1 
               
               
                      vl.right_edge = {vr2.category, INSERTED} 
               
               
                      // now add the new production 
               
               
                      let br = new BR_Production 
               
               
                      br.lhs = vl 
               
               
                      br.rhs.left_variable = vr1 
               
               
                      br.rhs.left_path = NIL 
               
               
                      br.rhs.left_role = HEAD 
               
               
                      br.rhs.right_variable = vr2 
               
               
                      br.rhs.right_path = NIL 
               
               
                      br.rhs.right_role = INSERTED 
               
               
                      br.probability = compute_prob(br) 
               
               
                      append br to result 
               
               
                     } 
               
               
                    } 
               
               
                   } 
               
               
                  } 
               
               
                  // attempt to extend vr2 by attaching vr1 
               
               
                  // make sure not attaching to a part-of-speech tag 
               
               
                  if (vr2.category is not a part-of-speech tag) { // e.g. as nn or dt_nn 
               
               
                   // make sure vr1 is a completed constituent 
               
               
                   if (vr2.complement_set == [ ]) { 
               
               
                    // make sure no more than 1 mismatched dependency 
               
               
                    if ((vr1.total_mismatches + vr2.total_mismatches) == 0) OR 
               
               
                     (vr2.missing_constituent is satisfied by vr1) // vr2 is missing vr1 
               
               
                    { 
               
               
                     // finally, make sure this really is a mismatched attachment 
               
               
                     if (NOT (vr1 satisfies some role in vr2.a_production.rhs)) { 
               
               
                      let vl = copy of vr2 
               
               
                      if (vr2.missing_constituent == NIL) { 
               
               
                       set vl.inserted_constituent with fields from vr1 
               
               
                      } else { 
               
               
                       vl.missing_constituent = NIL 
               
               
                      } 
               
               
                      vl.total_mismatches = 1 
               
               
                      vl.left_edge = {vr1.category, INSERTED} 
               
               
                      // now add the new production 
               
               
                      let br = new BR_Production 
               
               
                      br.lhs = vl 
               
               
                      br.rhs.left_variable = vr1 
               
               
                      br.rhs.left_path = NIL 
               
               
                      br.rhs.left_role = INSERTED 
               
               
                      br.rhs.right_variable = vr2 
               
               
                      br.rhs.right_path = NIL 
               
               
                      br.rhs.right_role = HEAD 
               
               
                      br.probability = compute_prob(br) 
               
               
                      append br to result 
               
               
                     } 
               
               
                    } 
               
               
                   } 
               
               
                  } 
               
               
                  // attempt to start new constituents with vr1 as head and vr2 attached 
               
               
                  // verify that vr1 and vr2 are both complete constituents 
               
               
                  if (vr1.complement_set == [ ]) and (vr2.complement_set == [ ]) { 
               
               
                   // make sure no more than 1 mismatched dependency 
               
               
                   if ((vr1.total_mismatches + vr2.total_mismatches) == 0) OR 
               
               
                    (vr1.missing_constituent is satisfied by vr2) // vr1 is missing vr2 
               
               
                   { 
               
               
                    let aprods3 = all A_Productions for this sentence where the head 
               
               
                           role is satisfied by vr1 and where vr2 satisfies NO 
               
               
                           modifier role 
               
               
                    forall ap in aprods3 { 
               
               
                     // add a BR_Production to the result that corresponds to ap 
               
               
                      let head_idx = index of ap&#39;s rhs that vr1 satisfies as the head 
               
               
                      // first create the lhs for the new production 
               
               
                      let vl = new B_Variable 
               
               
                      vl.category = ap.lhs.category 
               
               
                      vl.headword = ap.lhs.headword 
               
               
                      vl.complement_set = ap.rhs 
               
               
                      vl.left_edge = NIL 
               
               
                      vl.right_edge = {vr2.category, INSERTED} 
               
               
                      vl.a_production = ap 
               
               
                      if (vr1.missing_constituent == NIL) { 
               
               
                       set vl.inserted_constituent with fields from vr2 
               
               
                      } else { 
               
               
                       vl.inserted_constituent = NIL 
               
               
                      } 
               
               
                      vl.missing_constituent = NIL 
               
               
                      vl.total_mismatches = 1 
               
               
                      // now add the new production 
               
               
                      let br = new BR_Production 
               
               
                      br.lhs = vl 
               
               
                      br.rhs.left_variable = vr1 
               
               
                      br.rhs.left_path = ap[head_idx].path 
               
               
                      br.rhs.left_role = ap[head_idx].role 
               
               
                      br.rhs.right_variable = vr2 
               
               
                      br.rhs.right_path = NIL 
               
               
                      br.rhs.right_role = INSERTED 
               
               
                      br.probability = compute_prob(br) 
               
               
                      append br to result 
               
               
                     } 
               
               
                    } 
               
               
                   } 
               
               
                   // attempt to start new constituents with vr2 as head and vr1 attached 
               
               
                   // verify that vr1 and vr2 are both complete constituents 
               
               
                   if (vr1.complement_set == [ ]) and (vr2.complement_set == [ ]) { 
               
               
                    // make sure no more than 1 mismatched dependency 
               
               
                    if ((vr1.total_mismatches + vr2.total_mismatches) == 0) OR 
               
               
                     (vr2.missing_constituent is satisfied by vr1) // vr2 is missing vr1 
               
               
                    { 
               
               
                     let aprods4 = all A_Productions for this sentence where the 
               
               
                           head role is satisfied by vr2 and where vr1 
               
               
                           satisfies NO modifier role 
               
               
                     forall ap in aprods4 { 
               
               
                      // add a BR_Production to the result that corresponds to ap 
               
               
                      let head_idx = index of ap&#39;s rhs that vr2 satisfies as the head 
               
               
                      // first create the lhs for the new production 
               
               
                      let vl = new B_Variable 
               
               
                      vl.category = ap.lhs.category 
               
               
                      vl.headword = ap.lhs.headword 
               
               
                      vl.complement_set = ap.rhs 
               
               
                      vl.left_edge = {vr1.category, INSERTED} 
               
               
                      vl.right_edge = NIL 
               
               
                      vl.a_production = ap 
               
               
                      if (vr1.missing_constituent == NIL) { 
               
               
                       set vl.inserted_constituent with fields from vr1 
               
               
                      } else { 
               
               
                       vl.inserted_constituent = NIL 
               
               
                      } 
               
               
                      vl.missing_constituent = NIL 
               
               
                      vl.total_mismatches = 1 
               
               
                      // now add the new production 
               
               
                      let br = new BR_Production 
               
               
                      br.lhs = vl 
               
               
                      br.rhs.left_variable = vr1 
               
               
                      br.rhs.left_path = NIL 
               
               
                      br.rhs.left_role = INSERTED 
               
               
                      br.rhs.right_variable = vr2 
               
               
                      br.rhs.right_path = ap[head_idx].path 
               
               
                      br.rhs.right_role = ap[head_idx].role 
               
               
                      br.probability = compute_prob(br) 
               
               
                      append br to result 
               
               
                     } 
               
               
                    } 
               
               
                   } 
               
               
                  } 
               
               
                  return result 
               
               
                 } 
               
               
                 request_leaf_productions(B_Terminal tr) 
               
               
                  // returns a list of LF_Productions where the rhs is tr 
               
               
                 { 
               
               
                  let atags = the list of all part of speech tags from the A_Productions for 
               
               
                        this sentence // both simple, like nn, and compound, 
               
               
                        like dt_nn 
               
               
                  forall tag in atags { 
               
               
                   let vl = new B_Variable 
               
               
                   vl.category = tag 
               
               
                   vl.headword = vr.headword 
               
               
                   vl.complement_set = [ ] 
               
               
                   vl.left_edge = NIL 
               
               
                   vl.right_edge = NIL 
               
               
                   vl.a_production = NIL 
               
               
                   vl.missing_constituent = NIL 
               
               
                   vl.inserted_constituent = NIL 
               
               
                   vl. total_mismatches = 0; 
               
               
                   // now add the new production 
               
               
                   let lf = new LF_Production 
               
               
                   lf.lhs = vl 
               
               
                   lf.rhs = tr 
               
               
                   tp.probability = compute_prob(tp) 
               
               
                   append lf to result 
               
               
                  } 
               
               
                 }