Similar Sentence Retrieval for Machine Translation Based on Word-Aligned Bilingual Corpus

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1 Smlar Sentence Retreval for Machne Translaton Based on Word-Algned Blngual Corpus Wen-Han Chao and Zhou-Jun L School of Computer Scence, Natonal Unversty of Defense Technology, Chna, cwhk@63.com School of Computer Scence and Engneerng, Behang Unversty, Chna, lzj@buaa.edu.cn Abstract. In ths paper, we present a novel method to retreve the smlar examples from the corpus when gven an nput whch should be translated, n whch we use the word algnment between the blngual sentence par to measure the smlarty of the nput and the example. Introducton Measures of the sentence smlarty are very mportant n many NLP applcatons, such as machne translaton (MT), especally the example-based machne translaton (EBMT) [], nformaton retreval (IR) and text summarzaton etc. Recently, some researchers present a hybrd corpus-based machne translaton system[], whch s a statstcal machne translaton (SMT), whle usng an examplebased decoder, n whch the smlar translaton examples wll avod translatng from the scratch, and the smlar examples retreved wll affect the results greatly. When retrevng the smlar examples, most of the metrcs measure the smlarty of the two monolngual sentences. However, n a hybrd machne translaton system, the examples n the tranng corpus provde rch nformaton, such as word algnment. In ths paper, we provde a novel approach, whch use the blngual nformaton of the examples to retreve the smlar examples for an nput sentence. The Word-Algned Blngual Corpus for MT There are two types of corpus-based MT systems: Statstcal Machne Translaton (SMT) and EBMT. SMT obtans the translaton models durng tranng, and does not need the tranng corpus when decodng; whle EBMT retreves the smlar examples n the corpus when translatng. For both of them use the same corpus, we can generate a hybrd MT, whch s a SMT system, whle usng an example-based decoder. Generally, the hybrd MT system uses a word-algned blngual corpus, whch s a collecton of the word-algned sentence par, represented as (C, A,, where C s the source language sentence, and E s the target language sentence and A s the word algnment between C and E. H. L et al. (Eds.): AIRS 008, LNCS 4993, pp , 008. Sprnger-Verlag Berln Hedelberg 008

2 Smlar Sentence Retreval for Machne Translaton 579 A word algnment s defned as follows: gven a sentence par (C,, we defne a lnk l = (.. j, s.. t), where.. j represents a sequence of words n sentence C, where and j are poston ndexes,.e., +,..., j ; and s.. t represents a sequence of words n sentence E. So each lnk represents the algnment between the consecutve words n both of the sentences. A word algnment between a sentence par s a set of lnks A = { l, l,..., l n}. In the paper, the example refers to a trple (C, A,. We can merge two lnks l = (.. j, s.. t) and l = (.. j, s.. t) to form a larger lnk, f the two lnks are adjacent n both of the sentences,.e... j s adjacent to.. j where = j + or = j +, or.. j (or.. j ) s ε, so do the s..t to s..t. If the regon (.. j, s.. t) can be formed by mergng two adjacent lnks gradually, we call the regon s ndependent. Fgure (a) shows a word algnment example; the number below the word s the poston ndex. In the example, the regon (..3, 3..5) s ndependent. ² q « where s the3 nearest4 cassno5?6 (a) An example wth word algnment / the ² q / nearest «/ cassno / where s /? (b) An ITG tree Fg.. An ITG tree whch s derved from the ITG. A lne between the branches means an nverted orentaton, otherwse a straght one. In our hybrd MT system, the word algnment n the corpus must satsfy the Inverson Transducton Grammar (ITG) constrant[3], so that each word algnment wll form a bnary branchng tree, whch provdes a weak but effectve way to constran the re-orderng of the words. Fgure b llustrates an ITG tree formed from the algnment. Usng the word-algned blngual corpus, we can extract the phrase pars, called as blocks. Each block s formed by combnng one or more lnks, and must be ndependent. Then, we buld the correspondng translaton models usng the relatve frequences. 3 The Fast Retreval of Smlar Sentences We dvde the retreval of smlar examples nto two phases: Fast Retreval Phase: retrevng the smlar examples from the corpus quckly, and take them as canddates. The complexty should be not too hgh. Refnng Phase: refnng the canddates to fnd the most smlar examples. In ths secton, we wll descrbe how to retreve the smlar examples quckly through the followng three smlarty metrcs.

3 580 W.-H. Chao and Z.-J. L 3. The Matched Words Metrc Gven an nput sentence I = ww... wn and an example (C, A,, we calculate the number of the matched source words between the nput sentence and the source sentence C n the example frstly. * Match Sm ( Input, Example ) word word = () Len ( I ) + Len ( C, A, E ) where Match s the number of the matched words, Len (I) s the number of word words n I, and Len( C, A, s the number of the words n the n C. 3. The Matched Blocks Metrc Gven an nput sentence w ( {,,... n}). We use I w w... w =, we search the blocks for each word Bk n to represent the blocks, n whch for each block ( c, e), the source phrase c use the word w as the frst word, and the length of c s c = w k k,.e. the..( + ). In order to retreve the k, we frst get all the source phrases wth w as the frst word, and then fnd the blocks for each source phrase c n the translaton model. Consderng the complexty, we set M as the maxmum length of the source phrase (here M=3 or 5),.e., k M. For each c, there may exsts more than one blocks wth c as the source phrase, so we wll sort them by the probablty and keep the best N (here set N=5) blocks. Now we represent the nput sentence as: B σ ( I ) = { block block Bk, n, k M } () For example, n an nput sentence, the = {(, ), (, me ), (, my ), (, )}. Note, some B may be B Mne empty, e.g. B = φ In the same way, we represent the example ( C, A, as: k, snce no blocks wth as the source phrase. ϕ ( C, A, = { block block Bk, block A*, k M } (3) where * A represents the blocks whch are lnks n the algnment A or can be formed by mergng adjacent lnks ndependently. In order to accelerate the retreval of smlar examples, we generate the block set for each example durng the tranng process and store them n the corpus. Now, we can use the number of the matched blocks to measure the smlarty of the nput and the example:

4 Smlar Sentence Retreval for Machne Translaton 58 Sm block ( Input, Example ) = * Match B + B Input block Example (4) where Match block s the number of the matched blocks and Bk ( B k φ ) n (I ) ( C, A, B Input B s the number of σ, and Example s the number of the blocks n ϕ. Snce each block s attached a probablty, we can compute the smlarty n the followng way: * Pr ob ( b) Sm b prob ( Input, Example ) = (5) Input Example B + B So the fnal smlarty metrc for fast retreval of the canddates s: Smfast( Input, Example) = α Smblock + βsmword + γsmprob (6) where α + β + γ =. Zhao et al.[4] provdes a method to tune the weghts, but here we use mean values,.e. α = β = γ = / 3. Durng the fast retreval phase, we frst flter out the examples usng the Sm word, then calculate the Sm fast for each example left, and retreve the best N examples. 4 Refne the Canddates After retrevng the canddate smlar examples, we refne the canddates usng the swallow structure of the sentences, to fnd the best M smlar examples. 4. The Algnment Structure Metrc Gven the nput sentence I and an example ( C, A,, we frst search the matched blocks, at ths moment the order of the source phrases n the blocks must correspond wth the order of the words n the nput. As Fgure shows, the matchng dvdes the nput and the example respectvely nto several regons, where some regons are matched and some un-matched. And we take each regon as a whole and algn them between the nput and the example accordng to the order of the matched regons. For example, the regon (..3,3..5) n ( C, A, s un-matched, whch algns to the regon (..) n I. In ths way, we can use a smlar edt dstance method to measure the smlarty. We count the number of the Deleton/Inserton/Substtuton operatons, whch take the regon as the object. We set the penalty for each deleton and nserton operaton as, whle consderng the un-matched regon n the example may be ndependent or not, we set the penalty for substtuton as 0.5 f the regon s ndependent, otherwse as. E.g., the dstance s 0.5 for substtutng the regon (..3,3..5) to (..).

5 58 W.-H. Chao and Z.-J. L ² q « ŸÂ 3 4 where s the 3 nearest 4 cassno 5?6 (a) An example (b) An nput Fg.. An nput and an example. After matchng, there are three regons n both sdes, whch are ncluded n the lne box, where the regon (4..5,..) n the example matches the regon (..3) n the nput, so do (6..6,6..6) to (4..4). And the regon (..3,3..5) n the example should be substtuted to (..) n the nput. We get the metrc for measurng the structure smlarty of the I and (, A, C : Sm algn ( Input, Example D + I + S ) = R + R nput exmaple (7) where D, I, S are the deleton, nserton and substtuton dstances, respectvely. And the R nput and Rexmaple are the regon numbers n the nput and example. 4. The Semantc Metrc We calculate the semantc dstance usng the Tong_Y_C_C_Ln [6], whch s a Chnese semantc lexcon. In the lexcon, the semantcs of the words are dvded nto three levels, called large, mddle and small level, represented as the uppercase letter, lowercase letter, and nteger number respectvely, so that t can represent a semantc usng a unque code. Each code may nclude one or more words, and each word may have one or more codes, for t may have multple semantcs. For example, and have the same code Aa0, the code for s Aa03. Now, we can compute the semantc dstance between two words usng the codes they are marked: If the large level s dfferent, the dstance s 3; the mddle s ; and the small s ; f the codes are equal, the dstance s 0. Snce each word has more than one code, we wll choose the nearest dstance. Usng the semantc dstance of words, we can measure the semantc dstance of two regons n the nput and the example. mn [ Dstword _ sem( w, w )]/ 3 w reg w reg Dstreg _ sem( reg, reg) = (8) Len( reg) + Len( reg) Gven the same match as secton 4., the substtuton dstance s the sum of semantc dstance of the substtuton regons: S sem ( Input, Example ) = Dst reg _ sem ( regon nput, regon example ) (9) regons

6 Smlar Sentence Retreval for Machne Translaton 583 Then, we obtan the semantc metrc: Sm sem ( Input, Example ) = D + I + S R + R nput sem exmaple (0) In the end, we obtan the smlarty metrc, whch consders all of the above metrcs: Sm fnal ( Input, Example ) α ' Sm fast + β ' Sm algn + γ ' Sm sem = () where α ' + β ' + γ ' =. Here we also use the mean values as the weghts. 5 Experments We carred out experments on a Chnese-Englsh blngual corpus, whch s the tranng corpus n the open Chnese-Englsh translaton task of IWSLT007, consstng of the sentence-algned spoken language text for travelng. The tranng corpus conssts of 39,953 sentence pars, and the test set 489 Chnese sentences. We lowercased and stemmed the words n the Englsh sentences for preprocessng. Durng the tranng, we obtaned frstly the word algnments, whch satsfy the ITG constrant, for the sentence pars n the corpus; then bult the translaton models, and retreved and stored the blocks n each examples,.e. ϕ ( C, A,. 5. Evaluaton of the Retreval of Smlar Examples We evaluate our retreval method n manual way: obtan 00 nput sentences from the test set randomly, take them as nputs to retreve the smlar examples (0 examples for each nput), and verfy manually whether the retreved examples are smlar to the nputs. We defne a correct smlar example as the followng way: f the retreved example can be turned to the nput sentence by modfyng x ndependent regons, where each regon conssts of at most y blocks (here set y = ). To evaluate the two retreval phases, we also get the best 0 examples for the fast retreval phrase n the canddates, and compare wth the fnal 0 examples. Table shows the results. It shows the correct number and accuraces for both phases by changed the x. Wth x becomes larger, the accuraces are hgher. When x =, the accuraces are both 45%, because some of the nput sentences are very short, and both of the two phases wll fnd the exactly smlar examples. But when x becomes larger, the refned retreval wll get more accurate examples. 5. Evaluaton of the Translaton Our goal s to mprove the translaton qualty, so we combned the retreval module nto our hybrd MT system, and compared the translaton results wth other SMT systems. The results lst n Table. The frst column lsts the MT systems: the frst two systems are two SMT systems, where Moses[7] s a state-of-the-art SMT system, and SMT-CKY s our SMT system

7 584 W.-H. Chao and Z.-J. L wth a CKY style decoder; the last two systems are hybrd MT systems wth fast retreval module and refned retreval module. The second column lsts the Bleu scores[8] for these systems. The results show that our hybrd MT system acheves an mprovement when usng the new retreval module. We conclude our method to retreve the smlar examples s effectve. Table. The results for retreval of smlar sentences Test x = x = x = 3 Number Corr. Accu. Corr. Accu. Corr. Accu. Fast Retreval % % 55% 77.5% Refned Retreval % % 74 87% Table. The results of translaton System Bleu(%) Moses.6 SMT-CKY 8.33 Hybrd MT wth fast retreval Hybrd MT wth refned retreval Conclusons We present a method to retreve the smlar examples for the nput sentence n the hybrd MT systems. The approach makes good use of the word algnment nformaton contaned n the corpus. Snce the method consders the nternal translaton process contaned mplctly n each example, t helps to mprove the translaton qualty. In the future, we wll mprove the refnng metrc further, especally the semantc metrc, and consder how to tune the weghts. Also, we should consder how to use the retreved examples more effectvely. Acknowledgements Ths work s supported by the Natonal Scence Foundaton of Chna under Grants No , and References. Mandreol, F., Martogla, R., Tbero, P.: Searchng Smlar (Sub)Sentences for Example- Based Machne Translaton. In: Att del Decmo Convegno Nazonale su Sstem Evolut per Bas d Datt (SEBD 00 ), Isola d Elba, Italy (00). Watanabe, T., Sumta, E.: Example-based Decodng for Statstcal Machne Translaton. Machne Translaton Summt IX, (003)

8 Smlar Sentence Retreval for Machne Translaton Wu, D.: Stochastc Inverson Transducton Grammars and Blngual Parsng of Parallel Corpora. Computatonal Lngustcs 3(3), 374 (997) 4. Zhao, Y.Y., Qn, B., Lu, T., Su, Z.-Z.: Sentence Smlarty Computng Based on Mult- Features Fuson. In: The Proceedngs of JSCL 005, August 005, pp (005) 5. L, B., Lu, T., Qn, B., L, S.: Chnese Sentence Smlarty Computng Based on Semantc Dependency Parsng (003) 6. Me, J.J., Zhu, Y.M., Gao, Y.Q., Yn, H.X.: Tong Y C C Ln, nd edn., Shangha (996) 7. Moses, 8. Papnen, K., Roukos, S., Ward, T., Zhu, W.-J.: BLEU: a Method for Automatc Evaluaton of Machne Translaton. In: Proceedngs of the 40th Annual Meetng of the Assocaton for Computatonal Lngustcs (ACL), Phladelpha, July 00, pp (00)