Parser Self-Training for Syntax-Based Machine Translation

Transcription

1 arsr Slf-Training for Syntax-Basd Machin Translation Makoto Morishita, Koichi Akab, Yuto Hatakoshi Graham ubig, Koichiro Yoshino, Satoshi akamrua Graduat School of Information Scinc ara Institut of Scinc and Tchnology Takayama-cho, Ikoma-shi, ara, Japan Abstract In syntax-basd machin translation, it is known that th accuracy of parsing gratly affcts th translation accuracy. Slf-training, which uss parsr output as training data, is on mthod to improv th parsr accuracy. Howvr, bcaus parsing rrors caus noisy data to b mixd with th training data, automatically gnratd pars trs do not always contribut to improving accuracy. In this papr, w propos a mthod for slcting slf-training data by prforming syntaxbasd machin translation using a varity of pars trs, using automatic valuation mtrics to slct which translation is bttr, and using that translation s pars tr for parsr slftraining. This mthod allows us to automatically choos th trs that contribut to improving translation accuracy, improving th ffctivnss of slf-training. In xprimnts, w found that our slf-traind parsrs significantly improv a stat-of-th-art syntax-basd machin translation systm in two languag pairs. 1. Introduction In statistical machin translation (SMT), rprsntativ mthods includ hras-basd Machin Translation (BMT) [1], in which ach phras is translatd by th translation modl and rordrd to th appropriat targt languag ordr, and syntax-basd machin translation [2], which uss parts of syntactic pars trs for translation. Whil BMT gnrally achivs high accuracy on languag pairs with clos word ordr such as English-Frnch, syntax-basd machin translation tchniqus hav shown to allow for bttr translation accuracy on languag pairs with diffrnt word ordr such as English-Japans. Among th various mthods for syntax-basd translation, Tr-to-String (T2S) translation [3], which uss pars trs in th sourc languag, has bn rportd to achiv high translation accuracy whil maintaining translation spd [4]. Howvr, T2S translation uss parsr rsults in th sourc languag, so translation accuracy gratly dpnds on parsr accuracy. On mthod to amliorat this problm is Forst-to-String (F2S) translation [5], which considrs multipl pars trs during th dcoding procss. Evn F2S translation, howvr, is havily affctd by th accuracy of th parsr usd to gnrat th pars forst [4]. arsr slf-training is on mthod to improv parsr accuracy [6]. Slf-training first parss unannotatd sntncs using an xisting modl, thn uss ths automatically gnratd pars trs to rtrain th parsr. This allows th parsr to automatically adapt to th data usd for slf-training, incrasing covrag of vocabulary or syntactic structurs, and thus incrasing parsr accuracy. Howvr, on downsid of standard slf-training mthods is that automatically gnratd pars trs ar oftn incorrct, which rducs thir ffctivnss as training data. Whil thr has not bn much work on slf-training in th contxt of syntax-basd machin translation itslf, Katz- Brown t al. [7] hav proposd a mthod for slf-training in th contxt of syntactic pr-ordring. In this mthod, which thy call targtd slf-training, thy first gnrat multipl pars trs using a syntactic parsr, thn us ths trs to prform pr-ordring, scor th output by comparing to corrct pr-ordrd data, and slct th pars tr that has th highst scor. By using information about th corrct prordring to slct which pars tr to us, this mthod has th ability to rmov pars trs that rsult in incorrct prordrings, rducing nois in th training data. Howvr, on th down sid, making th manually alignd data rquird to apply this varity of targtd slf-training is costly, limiting its applicability to situations whr this data can b cratd. In this papr, w propos a mthod for targtd slftraining of parsrs for syntax-basd translation. Th proposd mthod is applicabl not only to pr-ordring but also syntax-basd MT, and has th additional advantag that it dos not rquir th prparation of costly hand-alignd training data bcaus it chooss data using standard MT automatic valuation mtrics. This allows for th us of xisting bilingual corpora as training data for targtd slf-training, making it possibl to improv parsrs and F2S translation accuracy in a widr varity of filds. By carrying out xprimnts on targtd slf-training considring machin translation accuracy, w confirmd that th proposd mthod signif-

2 icantly improvs th translation accuracy of a stat-of-th-art F2S systm in two languag pairs. 2. Tr-to-String translation In SMT, givn th sourc sntnc f, w considr th problm of finding translation ê that maximizs th postrior probability r( f) x 0 : ha V V V x 1 : V AUX V wo mi ta ê := argmaxr( f). (1) Among th varitis of SMT, T2S translation uss sourc languag pars tr T f to disambiguat th sourc structur and xprss th hirarchical rlationships btwn th sourc and targt languags as ruls, allowing for mor accurat translation. T2S translation can b formulatd as follows ê := argmaxr( f) (2) = argmax r( f,t f )r(t f f) (3) T f argmax r( T f )r(t f f) (4) T f argmaxr( ˆT f ), (5) whr ˆT f is th highst probability pars tr candidat rprsntd by th following formula: ˆT f = argmaxr(t f f). (6) T f As shown in Figur 1, translation ruls usd by T2S translation 1 ar rprsntd by th st of a sourc subtr and a targt languag string of words, including th rplacabl variabls x. In th xampl shown in Figur 1, x 0 and x 1 ar th rplacabl variabls. During translation, th dcodr finds th highst probability translation considring th probability of translation ruls, languag modls, or othr faturs. Th dcodr can also b usd to output th n translations with th highst probability,n-bst translations. In T2S translation, by taking th sourc languag pars tr into account, translation of long-distanc word ordring can b mor accurat than BMT. Howvr, bcaus T2S uss th pars tr for translation, th translation accuracy gratly dpnds on th parsr accuracy. As mntiond in th introduction, F2S translations rduc th advrs ffct of parsr rrors by using a pars forst, which is a hypr-graph fficintly xprssing a larg numbr of pars trs. By using a pars forst for translation, th dcodr can slct which pars tr to us from svral pars tr candidats, lading to improvd translation accuracy [9]. F2S translation can b formulatd as follows: ê, ˆT f = argmaxr( T f )r(t f f). (7),T f 1 Spcifically, T2S translation using tr transducrs [8]. x 0 saw a x 1 Figur 1: An xampl of a Japans-to-English tr-to-string translation rul Howvr, vn using F2S translation, th accuracy is havily affctd by th accuracy of th parsr usd to gnrat th pars forst [4]. In th following sctions, w dscrib som mthods to improv parsr accuracy. 3. arsr slf-training 3.1. Introduction of slf-training arsr slf-training rtrains th parsr using th pars trs automatically gnratd by an xisting modl, allowing th parsr to adapt to th data usd for slf-training and improv accuracy. In othr words, for ach sntnc usd for slftraining, w find th highst probability pars tr T ˆ f basd on Equation (6), thn us this pars tr to rtrain th parsr. Whn Charniak first proposd parsr slf-training, h rportd that a parsr using robabilistic Contxt-Fr Grammar (CFG) modls traind using th WSJ corpus [10] achivd no gain through slf-training [11]. On th othr hand, th CFG with Latnt Annotations (CFG-LA) modl, which achivs improvd parsing accuracy by using latnt annotations, has bn rportd to b improvd significantly by slf-training [12]. This is bcaus th CFG-LA has rlativly high accuracy, so th automatically gnratd pars trs usd for slf-training ar mor accurat, and bcaus th CFG-LA modl has mor paramtrs than standard CFGs, making it mor likly to bnfit from th incrasd amount of data. Basd on ths studis, w considr parsr slf-training using th CFG-LA modl Slf-training of th parsr in machin translation As mntiond in th introduction, thr is on prvious study on improving translation accuracy by doing parsr slftraining. Katz-Brown t al. propos a mthod for targtd slf-training, whr trs ar slctd basd on an xtrinsic valuation masur, and rport that it is possibl improv translation accuracy itslf [7]. Spcifically, thy automatically gnrat svral candidat pars trs, thn slct th candidat for which th pr-ordring rsult is most similar to hand-alignd corrct data. This tr is thn usd to rtrain th parsr. Formally, w dfin th pr-ordring function rord(t f ),

3 which gnrats a pr-ordrd sourc languag sntnc f basd on a pars trt f, and a scor function scor(f,f ) [13], which comparsf to th rfrnc prordrd sntnc f. ars tr T f, which is usd in slf-training, is slctd from th candidat pars trst f by th following formula T f = argmax T f T f scor(f, rord(t f )) (8) In this papr, basd on ths prvious studis, w propos a mthod for parsr targtd slf-training for syntax-basd translation. In th following sctions, w xplain th dtail of this mthod and vrify its ffctivnss. 4. arsr slf-training for syntax-basd MT An important point that dtrmins th ffctivnss of slftraining is how to slct th data usd to rtrain th parsr. In th following sctions, w propos svral mthods to slct pars trs and sntncs that ar ffctiv to improv th accuracy of F2S translation Slcting pars trs As dscribd in Sction 3.2, th targtd slf-training proposd by Katz-Brown t al. [7], slcts th most accurat pars tr from n-bst candidats by comparing hand-alignd corrct prordring data and automatically gnratd pars trs. Howvr, constructing hand-alignd data is costly, and thus it is impractical to crat larg data sts for this mthod. To solv this problm, w propos two mthods for targtd slf-training using only a paralll corpus. On is to us th pars tr usd in th 1-bst translation slctd by th dcodr, and th othr is to us th pars tr usd in th oracl translation, which is th most similar translation to th rfrnc translation from th n-bst list slctd by automatic valuation mtrics Dcodr 1-bst As dscribd in Sction 2, in F2S translation, th dcodr slcts th pars tr usd to gnrat th translation with th highst probability from th pars forst. A prvious study has notd that th F2S dcodr has th ability to slct mor accurat pars trs, as it uss othr fatur functions such as rul probabilitis and languag modl probabilitis, which cannot b considrd by th baslin parsr [9]. Thus, th pars tr usd in th on-bst translation could b mor ffctiv for slf-training than th parsr 1-bst tr. In this cas, th pars tr usd in slf-training is ˆT f in Formula (7) Automatic valuation 1-bst During translation, th dcodr outputs th translation that has th highst translation probability from a multitud of translation candidats. Howvr, thr ar also cass in which othr candidat translations, for xampl ons in th n-bst list, ar mor similar to th rfrnc translation, which indicats that thy may b mor accurat than th dcodr 1-bst translation. W dfin th oracl translationē, which is th closst to rfrnc translation among th n-bst translation candidats E. In this mthod, w prform slf-training using th pars tr that is usd in th oracl translation ē. By using th scor function scor( ), which rprsnts th similarity btwn th hypothsis and rfrnc translation, ē is formulatd as follows: 4.2. Slcting sntncs ē = argmaxscor(,). (9) E In Sction 4.1, w dscribd mthods to slct, from an n- bst list for a singl sntnc, pars trs that may b usful in slf-training. Howvr, in many cass, th corrct pars tr may not b includd in th n-bst translation candidats, and thr is a possibility of ths sntncs adding nois to th training data. Thrfor, w furthr propos two mthods for slcting which sntncs should b usd in slf-training from th ntirty of th training data, potntially rmoving sntncs for which no good n-bst candidat xists. On is to us sntncs for which th translatd sntnc s automatic valuation scor xcds a thrshold, and th othr is to us sntncs that hav a larg scor incras btwn th dcodr 1-bst and oracl translation Automatic valuation thrshold Thr ar som sntncs in th corpus that ar not translatd accuratly by th MT systm, and th scor of automatic valuation mtrics dcrass. Th caus of low valuation scors could b for th th following rasons: An incorrct pars tr has bn usd in th translation. Th translation modl dos not sufficintly covr th sourc sntnc s vocabulary or phrass. Th rfrnc translation, which is usd to calculat th automatic valuation scor, is a fr or incorrct translation, and th systm cannot output a similar translation. In such cass, th scor of automatic valuation mtrics will b low vn for th oracl translation. Bcaus th F2S dcodr cannot slct th corrct trs or th valuation scors ar not rliabl, ths data ar mor likly to hav noisy oracl trs for training, and thus it is potntially bnficial to xclud ths trs from th training data. For ths rasons, w propos a sntnc slction mthod that uss only sntncs that achiv scors ovr a thrshold, which can b xpctd to hav mor accurat pars trs. Th st of sntncs for slf-training is dfind as follows, whr t is th thrshold, (i) is th rfrnc translation of th sntnc i, ē (i) is th oracl translation of th sntnc i, Ē is

4 th st of all oracl translations, and scor() is th automatic valuation scor function {i scor( (i),ē (i) ) t, ē (i) Ē}. (10) Automatic valuation gain xt, w focusd on th diffrnc btwn th automatic valuation scor of th dcodr 1-bst and oracl translation. In th cas that th pars forst output by th parsr has incorrct probabilitis for th pars trs, in many cass, th dcodr will slct th incorrct pars tr and output th wrong translation. On th othr hand, th oracl translation is mor likly to hav usd a corrct pars tr from th pars forst. Thrfor, by using th pars trs usd in oracl translations as training data in ths cass, it may b possibl to improv th parsr s probability stimats. This will rsult in th systm using th slf-traind parsr tnding to output th corrct translation as a 1-bst, improving translation accuracy. To slct th sntncs, w dfin th function gain(ē (i),ê (i) ), which rprsnts th gain btwn th scor of 1-bst translation ê (i) and oracl translation ē (i), thn slcts th sntncs with highst gain as in Formula (10). Th function gain(ē (i),ê (i) ) is formulatd as follows: gain(ē (i),ê (i) )=scor(ē (i) ) scor(ê (i) ). (11) In addition, in this mthod, in ordr to nsur that th sntnc lngth distribution of th training data is similar to that of th ntir corpus, w us th following formula proposd by Gascó t al. [14], to nsur that th lngth distribution of th slctd sntncs is similar to that of th ovrall corpus distribution. 2 In this formula, is th lngth of targt languag sntnc, f is th lngth of sourc languag sntnc f, c ( + f ) is th numbr of sntncs in th corpus with lngth + f, and c is th numbr of sntncs in th ntir corpus p( + f )= c( + f ) c. (12) Th numbr of sntncs slctd for th slf-training st is formulatd as follows, whr t ( + f ) is th numbr of sntncs in th slf-training st with lngth + f, and t is th numbr of sntncs in th ntir slf-training st 5.1. Exprimntal stup t ( + f )=p( + f ) t. (13) 5. Exprimnts In th xprimnts, w focusd on Japans-English and Japans-Chins translation. Bcaus th amount of handlabld Japans pars tr data is lss than that availabl for English, th Japans parsr is pron to pars rrors. As th translation data, w us ASEC, 3 which is a paralll cor- 2 Th BLEU gain approach was not ffctiv if w did not us this tchniqu, as it tnds to slct only short sntncs whr small changs in wording caus larg changs in valuation scors. 3 Tabl 1: Th numbr of sntncs in ASEC Train Dv DvTst Tst Ja-En 2,000,000 1,790 1,784 1,812 Ja-Zh 672,315 2,090 2,148 2,107 pus of scintific paprs abstracts. Th numbr of sntncs in ASEC is shown in Tabl 1. 4 As a stat-of-th-art baslin for vrifying th ffct of slf-training, w us th systm dvlopd by ubig [15], 5 which was th most accurat systm on th Workshop on Asian Translation (WAT) 2014 [16]. W us Travatar [17] as a Forst-to-String dcodr. As a parsr, w us th CFG-LA parsr Egrt, 6 and train a baslin modl on a phras-structur vrsion of th Japans Dpndncy Corpus (JDC) [18], which has about 7000 sntncs. Forsts wr prund to rmov hypr-dgs which do not appar in th 100n-bst trs. Egrt somtims fails to output a pars tr, and in this cas, w rmov th faild sntncs. W valuat th accuracy by using two automatic valuation mtrics, BLEU [19] and RIBES [20], and to valuat th accuracy for ach sntnc in sntnc or oracl slction, w us BLEU+1 [21]. For slf-training data, w add th data slctd from th ASEC training data to th JDC trs. Th training data for th translation systms is parsd using th standard JDC modl, and th slf-traind modls ar usd only to pars th dvlopmnt and tst corpora at tst tim. 7 W vrify statistical significanc using th bootstrap rsampling mthod [22]. In th nxt sction, w compar th following parsr slf-training mthods: arsr 1-bst As in Formula (6), w us th 1-bst pars trs for slf-training. W slct th sntncs randomly from th corpus. 8 MT 1-bst As dscribd in Sction 4.1.1, w input th pars forst to th dcodr, and us th pars trs usd in th 1-bst translation. W slct th sntncs randomly from th corpus as in arsr 1-bst. Oracl As dscribd in Sction 4.1.2, w input th pars forst to th dcodr, output uniqu 500-bst hypothss and us th pars tr corrsponding to th translation that has th highst BLEU+1 scor in this n-bst list. W 4 ASEC actually has 3.0 million Ja-En training sntncs, but bcaus th data was automatically alignd, w us only th highst-confidnc 2.0 million sntncs to maintain th quality of th training data It may b possibl to furthr improv translation accuracy by r-parsing th training data, but this coms at a significant computational cost, so in this work w only xprimnt with r-parsing th dvlopmnt and tst corpora. 8 Whil a larg corpus is availabl, training th parsr using th ntir corpus is computationally xpnsiv, so w randomly subsampl a training corpus.

5 Tabl 2: Exprimnt rsults of Japans-English translation Ja-En Ja-Zh Sntnc slction Tr slction Snt BLEU RIBES Snt BLEU RIBES (a) (b) Random arsr 1-bst 96k k (c) Random MT 1-bst 97k k (d) Random BLEU+1 1-bst 97k k () BLEU BLEU+1 1-bst 206k k (f) BLEU BLEU+1 1-bst 120k k (g) BLEU BLEU+1 1-bst 58k k (h) BLEU+1 Gain BLEU+1 1-bst 100k k (i) BLEU (Ja-En) BLEU+1 1-bst 120k Sntncs 25k" 20k" 15k" 10k" 5k" 0" 0.0"" 0.1"" 0.2"" 0.3"" 0.4"" 0.5"" 0.6"" 0.7"" 0.8"" 0.9"" BLEU+1-Scor Ja0En" Ja0Zh" Figur 2: BLEU+1 scor distribution of translations in Tabl 2 (d). slct th sntncs randomly from th corpus as in arsr 1-bst. Oracl (BLEU+1 t) As dscribd in Sction 4.2.1, among th oracl translations and pars trs, w only us th sntncs for which th BLEU+1 scor xcds th thrsholdt. BLEU+1 Gain As dscribd in Sction 4.2.2, among th oracl translations and pars trs, w us only th sntncs which hav a larg diffrnc of BLEU+1 scor btwn 1-bst and oracl translations. In this mthod, w maintain th sntnc lngth distribution by using Formula (12) and (13). It should b notd that whn slcting th sntncs randomly, w slct 1/20 of all training data in Japans- English, 1/10 in Japans-Chins translation. In BLEU+1 Gain, w slct th top 100k sntncs, which is a similar numbr of sntncs as usd in th othr mthods Exprimnt rsults Tabl 2 shows th xprimntal rsults for Japans-English and Japans-Chins translation. Th daggr symbol in th tabl indicats that th translation accuracy of th proposd mthod is significantly highr than th baslin ( : p< 0.05, : p<0.01). In Tabl 2 (b), (c), (d), th sntncs for slf-training ar th sam xcpt whr Egrt fails to pars. 9 In Tabl 2, Snt indicats th numbr of sntncs addd through slf-training and dos not includ th xisting JDC data. In our analysis, w mainly focus on th BLEU scor rsults, bcaus w usd BLEU+1 as a critrion whn picking sntncs for slf training. Basd on ths rsults, w answr th following rsarch qustions: Is targtd slf training through pars tr slction (Sction 4.1) ffctiv in improving translation rsults? Can sntnc slction (Sction 4.2) furthr rduc nois and improv accuracy? Is slf-training languag dpndnt, or portabl across targt languags? Effct of Tr Slction: First, w can s that th mthod of using parsr 1-bst trs as slf-training data did not achiv a BLEU scor improvmnt in Ja-En and Ja-Zh translation (Tabl 2 (b)). Additionally, whil in th MT 1- bst mthod, th accuracy is improvd compard to arsr 1- bst in th Ja-En xprimnt, thr is no improvmnt compard to th baslin systm. In th Ja-Zh xprimnt, MT 1-bst is almost th sam as parsr 1-bst (Tabl 2 (c)). W manually analyzd th pars trs that hav bn usd for slf-training in ths mthods, and whil thr ar som corrct trs thr ar also many incorrct trs, which likly disturbd th training. xt looking at th BLEU+1 1-bst scors (Tabl 2 (d)), w can s that by slcting pars trs that wr usd in th oracl translations, BLEU scors slightly improvd in both Ja-En and Ja-Zh xprimnts, with th Ja-Zh systm significantly outprforming th baslin. Figur 2 shows th BLEU+1 scor distribution of oracl translations usd in slftraining in this cas. Th labl on th horizontal axis rpr- 9 In th mthods xcpt (b), w us Ckylark [23] traind by JDC as an altrnativ parsr whn Egrt fails to pars.

6 Tabl 3: Slf-traind Japans parsr accuracy Sntnc slction Tr slction Rcall rcision F-Masur (a) (b) Random arsr 1-bst (c) BLEU BLEU+1 1-bst Tabl 4: An xampl of an improvmnt in Japans-English translation Sourc Rfrnc in th C - administrd group, thrmal raction clarly incrasd th activity of R for 240 minuts. Baslin for 240 minuts clarly nhancd th activity of C administration group R. BLEU for 240 minuts clarly nhancd th activity of R in th C - administration group. AUX SYM SYM C AUX SYM administrd SYM AUX V group in SYM TO SYM (a) ars trs of th baslin systm R of activity SYM C OBJ administrd group in TO V SYM R ADV of (b) ars tr of th slf-traind systm activity V OBJ V Figur 3: An xampl of an improvmnt in parsing rsult snts th numbr of sntncs which hav BLEU+1 scors gratr thanxand lss thanx+0.1, whrxis th labl. As can b sn from th figur, thr ar many sntncs whr vn th oracl has a low scor, motivating th sntnc slction mthods prsntd in Sction 4.2. Effct of Sntnc Slction: xt, w xamin th ffct of slcting sntncs using th BLEU+1 scor thrshold. From th rsults, w find it ffctiv, with translation accuracy improving spcially in Ja-En xprimnts (Tabl 2 (),(f),(g)). In th Ja-Zh xprimnts, th BLEU+1 scor distribution of oracl translations tnds to b highr than in th Ja-En translations, xplaining why this mthod is mor ffctiv in Ja-En xprimnts. From this rsult, w can say that whn doing parsr slf-training, it is important to rmov th low accuracy pars trs and kp only high accuracy pars trs from th training data. This is particularly tru whn thr ar a larg numbr of oracl translations with low accuracis. Morovr, by using th data that hav a larg BLEU+1 scor improvmnt btwn MT 1-bst and oracl translations, w achivd an ffct similar to that of th BLEU+1 thrshold mthod (Tabl 2 (h)). Targt Languag ortability: Finally, w xamin what happns whn a parsr usd for translation in on languag pair, Japans-English, is usd to pars th sntncs for translation in anothr languag pair, Japans-Chins (Tabl 2 (i)). Intrstingly, th improvmnt in this cas is quit similar to th parsr traind dirctly on th Japans- Chins data. Thus, th modl s dpndnc on targt languag is not strong, and it may b possibl to do mor ffctiv slf-training by using svral targt languags as training data Exampl of improvd translation Tabl 4 shows an xampl of an improvmnt causd by slftraining in Japans-English translation. In addition, Figur

7 3 shows th pars trs usd in th translations in Tabl 4. In this sntnc C (C administration group) and R (activity of R) ar noun phrass. Th baslin parsr cannot idntify noun phrass corrctly, and translation is affctd by this pars rror. On th othr hand, th slftraind parsr can idntify noun phrass corrctly, rsulting in ths phrass bing corrctly translatd Slf-traind parsr accuracy W also prformd xprimnts to xamin th parsr accuracy itslf. W manually cratd 100 rfrnc pars trs from th Ja-En ASEC tst data, and chckd th accuracy of th baslin and slf-traind parsrs with rspct to ths trs by using Evalb. 10 Tabl 3 shows th xprimntal rsults. Th daggr symbol in th tabl indicats that th F- Masur of th proposd mthod is significantly highr than th baslin ( : p<0.05, : p<0.01). Hr, w can s that th parsr 1-bst mthod achivd significantly highr accuracy than th baslin at th 95% lvl. In addition, our proposd targtd slf-traind parsr could achiv a furthr significant gain in accuracy. Ths rsults show that our proposd targtd slf-training mthods improv not only MT rsults, but also parsr accuracy itslf. 6. Conclusion In this study, w proposd a targtd slf-training mthod for syntactic parsrs usd in syntax-basd MT, and vrifid its ffct on T2S translation. W prformd xprimnts on Japans-English and Japans-Chins translation and found that by using th slf-traind parsr that w wr abl to achiv a significant improvmnt in th accuracy of a stat-of-th-art translation systm. Morovr, w found that th modl slf-traind by Japans-English sntncs can also contribut to mor accurat Japans-Chins translations. Our futur work includs vrifying that this mthod can b usd for othr languags pairs. Morovr, th xprimntal rsults suggst that th ffct of slf-training dos not havily dpnd on th sourc languag, and thus it may b possibl to improv th translation accuracy by applying slftraining ovr data from multipl languags pairs. Furthrmor, w will tst th ffct on translation accuracy whn prforming multipl itrations of parsr slf-training, or using th slf-traind parsr to r-pars th training data. 7. Acknowldgmnts art of this rsarch was supportd by JSS KAKEHI Grant umbr and Rfrncs [1]. Kohn, F. J. Och, and D. Marcu, Statistical phrasbasd translation, in roc. HLT, 2003, pp [2] K. Yamada and K. Knight, A syntax-basd statistical translation modl, in roc. ACL, 2001, pp [3] Y. Liu, Q. Liu, and S. Lin, Tr-to-string alignmnt tmplat for statistical machin translation, in roc. ACL, 2006, pp [4] G. ubig and K. Duh, On th lmnts of an accurat tr-to-string machin translation systm, in roc. ACL, 2014, pp [5] H. Mi, L. Huang, and Q. Liu, Forst-basd translation, in roc. ACL, 2008, pp [6] D. McClosky, E. Charniak, and M. Johnson, Effctiv slf-training for parsing, in roc. HLT, 2006, pp [7] J. Katz-Brown, S. trov, R. McDonald, F. Och, D. Talbot, H. Ichikawa, M. Sno, and H. Kazawa, Training a parsr for machin translation rordring, in roc. EML, 2011, pp [8] J. Grahl and K. Knight, Training tr transducrs, in roc. HLT, 2004, pp [9] H. Zhang and D. Chiang, An xploration of forst-tostring translation: Dos translation hlp or hurt parsing? in roc. ACL, 2012, pp [10] M.. Marcus, M. A. Marcinkiwicz, and B. Santorini, Building a larg annotatd corpus of English: Th nn trbank, Computational linguistics, vol. 19, no. 2, pp , [11] E. Charniak, Statistical parsing with a contxt-fr grammar and word statistics, in roc. AAAI, 1997, pp [12] Z. Huang and M. Harpr, Slf-training CFG grammars with latnt annotations across languags, in roc. EML, 2009, pp [13] D. Talbot, H. Kazawa, H. Ichikawa, J. Katz-Brown, M. Sno, and F. Och, A lightwight valuation framwork for machin translation rordring, in roc. WMT, 2011, pp [14] G. Gascó, M.-A. Rocha, G. Sanchis-Trills, J. Andrés- Frrr, and F. Casacubrta, Dos mor data always yild bttr translations? in roc. ACL, 2012, pp [15] G. ubig, Forst-to-string SMT for asian languag translation: AIST at WAT2014, in roc. WAT, 2014.

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