Tree-to-Tree]

A Syntax-based Statistical Translation ModelKenji Yamada and Kevin KnightInformation Sciences InstituteUniversity of Southern California4676Admiralty Way,Suite1001Marina del Rey,CA90292kyamada,knight@AbstractWe present a syntax-based statisticaltranslation model.Our model trans-forms a source-language parse treeinto a target-language string by apply-ing stochastic operations at each node.These operations capture linguistic dif-ferences such as word order and casemarking.Model parameters are esti-mated in polynomial time using an EMalgorithm.The model produces wordalignments that are better than thoseproduced by IBM Model5.1IntroductionA statistical translation model(TM)is a mathe-matical model in which the process of human-language translation is statistically modeled. Model parameters are automatically estimated us-ing a corpus of translation pairs.TMs have been used for statistical machine translation(Berger et al.,1996),word alignment of a translation cor-pus(Melamed,2000),multilingual document re-trieval(Franz et al.,1999),automatic dictionary construction(Resnik and Melamed,1997),and data preparation for word sense disambiguation programs(Brown et al.,1991).Developing a bet-ter TM is a fundamental issue for those applica-tions.Researchers at IBMfirst described such a sta-tistical TM in(Brown et al.,1988).Their mod-els are based on a string-to-string noisy channel model.The channel converts a sequence of words in one language(such as English)into another (such as French).The channel operations are movements,duplications,and translations,ap-plied to each word independently.The movement is conditioned only on word classes and positions in the string,and the duplication and translation are conditioned only on the word identity.Math-ematical details are fully described in(Brown et al.,1993).One criticism of the IBM-style TM is that it does not model structural or syntactic aspects of the language.The TM was only demonstrated for a structurally similar language pair(English and French).It has been suspected that a language pair with very different word order such as En-glish and Japanese would not be modeled well by these TMs.To incorporate structural aspects of the lan-guage,our channel model accepts a parse tree as an input,i.e.,the input sentence is preprocessed by a syntactic parser.The channel performs oper-ations on each node of the parse tree.The oper-ations are reordering child nodes,inserting extra words at each node,and translating leaf words. Figure1shows the overview of the operations of our model.Note that the output of our model is a string,not a parse tree.Therefore,parsing is only needed on the channel input side.The reorder operation is intended to model translation between languages with different word orders,such as SVO-languages(English or Chi-nese)and SOV-languages(Japanese or Turkish). The word-insertion operation is intended to cap-ture linguistic differences in specifying syntactic cases.E.g.,English and French use structural po-sition to specify case,while Japanese and Korean use case-marker particles.Wang(1998)enhanced the IBM models by in-troducing phrases,and Och et al.(1999)used templates to capture phrasal sequences in a sen-tence.Both also tried to incorporate structural as-pects of the language,however,neither handles1. Channel Input3. Inserted2. Reorderedkare ha ongaku wo kiku no ga daisuki desu5. Channel Output4. TranslatedVBPRP VB1VB2VBTO TONNVBVB2TOVB1VBPRPNNTOVBVB2TOVBVB1PRPNNTOVBVB2TOVBPRPNNTOVB1n−table r−tablet−tableTable1:Model Parameter Tablesparent=VB node=PRP is the conditioning in-ing this label pair captures,for example,the regularity of inserting case-marker particles.When we decide which word to insert,no condi-tioning variable is used.That is,a function wordlike ga is just as likely to be inserted in one placeas any other.In Figure1,we inserted four words(ha,no,ga and desu)to create the third tree.Thetop VB node,two TO nodes,and the NN nodeinserted nothing.Therefore,the probability ofobtaining the third tree given the second tree is3.498e-9.Finally,we apply the translate operation toeach leaf.We assume that this operation is depen-dent only on the word itself and that no contextis consulted.2The model’s t-table specifies theprobability for all cases.Suppose we obtained thetranslations shown in the fourth tree of Figure1.The probability of the translate operation here is.The total probability of the reorder,insert andtranslate operations in this example is3.498e-9 1.828e-11.Note that thereto reorder them.This operation applies only to non-terminal nodes in the tree.Translation is an operation that translates a terminal English leaf word into a French word.This operation applies only to terminal nodes.Note that an English word can be translated into a French NULL word. The notation stands for a set of values of.is a set of values of random variables associated with .And is the set of all ran-dom variables associated with a parse tree.The probability of getting a French sentence given an English parse tree isPStrPwhere Str is the sequence of leaf words of a tree transformed by from.The probability of having a particular set of values of random variables in a parse tree isP PPThis is an exact equation.Then,we assume that a transform operation is independent from other transform operations,and the random variables of each node are determined only by the node itself. So,we obtainP PPThe random variables are as-sumed to be independent of each other.We also assume that they are dependent on particular fea-tures of the node.Then,P PP P PP P Pwhere,,and are the relevant features to ,,and,respectively.For example,we saw that the parent node label and the node label were used for,and the syntactic category sequence of children was used for.The last line in the above formula introduces a change in notation, meaning that those probabilities are the model pa-rameters,,and,where,, and are the possible values for,,and, respectively.In summary,the probability of getting a French sentence given an English parse tree is PStrPStrwhere and.The model parameters,,and ,that is,the probabilities P,P and P,decide the behavior of the translation model,and these are the probabilities we want to estimate from a training corpus.2.3Automatic Parameter EstimationTo estimate the model parameters,we use the EM algorithm(Dempster et al.,1977).The algorithm iteratively updates the model parameters to max-imize the likelihood of the training corpus.First, the model parameters are initialized.We used a uniform distribution,but it can be a distribution taken from other models.For each iteration,the number of events are counted and weighted by the probabilities of the events.The probabilities of events are calculated from the current model pa-rameters.The model parameters are re-estimated based on the counts,and used for the next itera-tion.In our case,an event is a pair of a value of a random variable(such as,,or)and a feature value(such as,,or).A separate counter is used for each event.Therefore,we need the same number of counters,,,and, as the number of entries in the probability tables, ,,and.The training procedure is the following:1.Initialize all probability tables:,,and.2.Reset all counters:,,and.3.For each pair in the training corpus,For all,such that Str,Let cnt=P Str PFor,+=cnt+=cnt+=cnt4.For each,,and,5.Repeat steps2-4for several iterations.A straightforward implementation that tries all possible combinations of parameters,is very expensive,since there are possi-ble combinations,where and are the num-ber of possible values for and,respectively( is uniquely decided when and are given for a particular).Appendix describes an efficient implementation that estimates the probability in polynomial time.3With this efficient implemen-tation,it took about50minutes per iteration on our corpus(about two thousand pairs of English parse trees and Japanese sentences.See the next section).3ExperimentTo experiment,we trained our model on a small English-Japanese corpus.To evaluate perfor-mance,we examined alignments produced by the learned model.For comparison,we also trained IBM Model5on the same corpus.3.1TrainingWe extracted2121translation sentence pairs from a Japanese-English dictionary.These sentences were mostly short ones.The average sentence length was6.9for English and9.7for Japanese. However,many rare words were used,which made the task difficult.The vocabulary size was 3463tokens for English,and3983tokens for Japanese,with2029tokens for English and2507 tokens for Japanese occurring only once in the corpus.Brill’s part-of-speech(POS)tagger(Brill, 1995)and Collins’parser(Collins,1999)were used to obtain parse trees for the English side of the corpus.The output of Collins’parser washe adores listening tomusichehas unusual ability inenglishhe wasablaze withangerhe adoreslistening tomusiche has unusual ability inenglishhe was ablaze with angerFigure2:Viterbi Alignments:our model(left)and IBM Model5(right).Darker lines are judged more correct by humans.The result was the following;Perfectsents0.5820.431in .If is a non-terminal node,a -subnodeconnects to several -subnodes,showing a selection of a value .The weight of the arc is P .A -subnode is then connected to -subnodes.The partition variable,,showsa particular way of partitioning .A -subnode is then connected to major-nodes which correspond to the childrenof and the substring of ,decided by.A major-node can be connected from different -subnodes.The arc weights between -subnodes and major-nodes are always 1.0.νP ρP π(ρ|ε)(ν|ε)Figure4:Formulae for alpha-beta probabilities,and the count derivationM.Collins.1999.Head-Driven Statistical Models for Natural Language Parsing.Ph.D.thesis,Univer-sity of Pennsylvania.A.Dempster,ird,and D.Rubin.1977.Max-imum likelihood from incomplete data via the em algorithm.Royal Statistical Society Series B,39. 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