| { |
| "paper_id": "C94-1006", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T12:49:20.670626Z" |
| }, |
| "title": "Two Methods for Learning ALT-J/E ]\u00a5anslation Rules from Examples and a Semantic Hierarchy llussein Ahnuallim ln[o. and Coml)uter Science Dept. King Fahd University of l)etroleum and Minerals l)hahran 312(;1, Sated( lXrahia", |
| "authors": [ |
| { |
| "first": "Yasuhiro", |
| "middle": [], |
| "last": "Akil", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "NTT Communication Science Labs", |
| "location": {} |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Takefumi", |
| "middle": [], |
| "last": "Yamazaki", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "NTT Communication Science Labs", |
| "location": {} |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Akio", |
| "middle": [], |
| "last": "Yok0o", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "NTT Communication Science Labs", |
| "location": {} |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Shigeo", |
| "middle": [], |
| "last": "Kalmda", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "NTT Communication Science Labs", |
| "location": {} |
| }, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "This palu:r prose.his our work towards the mtlomatic acquisition of translatiort \"t'ules from Jatmnese-l')nglish transhdion examples fo'r NTT\"s ALT'-J/I'2 .machine translation system. We apply two lttat:hinc lca'tvti~ 9 ab.loritim~s : lIaussler's algm'ithm fro\" h:mvvirtg internal disj'tmctive concept and (~'uirdan's I1)3 algm'ithm. l,;:Cl)evimental results show that our al)trroach yields r'uh'.s that (lI'('. highly a(:c'twale colnpalvd to l]tc l/lilly(tally crv.atcd r'ules.", |
| "pdf_parse": { |
| "paper_id": "C94-1006", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "This palu:r prose.his our work towards the mtlomatic acquisition of translatiort \"t'ules from Jatmnese-l')nglish transhdion examples fo'r NTT\"s ALT'-J/I'2 .machine translation system. We apply two lttat:hinc lca'tvti~ 9 ab.loritim~s : lIaussler's algm'ithm fro\" h:mvvirtg internal disj'tmctive concept and (~'uirdan's I1)3 algm'ithm. l,;:Cl)evimental results show that our al)trroach yields r'uh'.s that (lI'('. highly a(:c'twale colnpalvd to l]tc l/lilly(tally crv.atcd r'ules.", |
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| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "A critical issue in AI r(~sem'ch is to ov(.'r(:(ml(~ the knowh~(Ige acquisition bottleneck in knowl(!dge-tms(!d systems. As a knowledge base is eXlmn(led, adding more kn((wl(~dg(-` and fixing previ(ms err(m(~(Tus kn()w1edge become increasingly c(Tstly. Mor(~(Tv(w, maintaining the integrity of Ire'go knowledge bases has 17rovcn to be a very chall(mging task.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "A wid(!ly im)i)(Tsed apl)roach t() deal with the knowl(~dg(~ a(:quisiti(m botth.uu~(:k is to employ some lcai'ning lll(-`ch}llliSi [l to (~Xtl'}lct th(~ ([csir(!d kni) wledge autornati(:a]ly or semi-automatically from a(:tual (:ases (Tr examl)h!s [lhmhamm & \\Vilkins ] 993]. The validity of this apiTroa('h is 17ec(TminI', m()re ew (dent as vari(ms machin,~-learning,-l)ased l~u()wh,(lg(' acquisitioi~ tools for real--world domains are l)(,i(l~,, report (-`d [Kim & Moldovan 1993, l) orter ~t al. 199t), Sato 1991a, 5;at(7 19!)ll7, Ul:sur(7 (!t al. 1992 , Wilkins 1990 ].", |
| "cite_spans": [ |
| { |
| "start": 131, |
| "end": 167, |
| "text": "[l to (~Xtl'}lct th(~ ([csir(!d kni)", |
| "ref_id": null |
| }, |
| { |
| "start": 247, |
| "end": 268, |
| "text": "[lhmhamm & \\Vilkins ]", |
| "ref_id": null |
| }, |
| { |
| "start": 454, |
| "end": 483, |
| "text": "(-`d [Kim & Moldovan 1993, l)", |
| "ref_id": null |
| }, |
| { |
| "start": 532, |
| "end": 553, |
| "text": "Ul:sur(7 (!t al. 1992", |
| "ref_id": null |
| }, |
| { |
| "start": 554, |
| "end": 568, |
| "text": ", Wilkins 1990", |
| "ref_id": null |
| } |
| ], |
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| "section": "Introduction", |
| "sec_num": "1" |
| }, |
| { |
| "text": "AIJI'-.J/I:'~, whi(:h is an exp(!rim(mtal Japan('s(!-English translation system d(~v(.qoped art Nipp(m 'lbh!gral)h and T(~lel)hon ('. Corporation (NTT) , is (me ex-amI)le of a larg(! knowh~dg(>l,ased system in which solutions t(7 the l~n()wle(lg (~ a('(luisiti(m l) So far, AI;F-J/E translation ruh!s have b(!en composed mam(ally by (~xtensiv(~ly trained human exl)('rts. T(7 qualify lln\" this.i(~b, an eXl~ert must not only master both English and .lapanes(~ but also be very familiar with various comi)onents of the system. Each tinm the rules are (~xi)anded or altc.r(-`d, the new set of rules must then I)c \"delmgg(~d\" using a c(711ecthm of t (.~I. ('as(,s. Usually, s('vcral it(~ri~tions are n(~cded t(7 arrive at translation rules (Tf acceptalflc quality.", |
| "cite_spans": [ |
| { |
| "start": 130, |
| "end": 151, |
| "text": "('. Corporation (NTT)", |
| "ref_id": null |
| }, |
| { |
| "start": 246, |
| "end": 265, |
| "text": "(~ a('(luisiti(m l)", |
| "ref_id": null |
| }, |
| { |
| "start": 647, |
| "end": 679, |
| "text": "(.~I. ('as(,s. Usually, s('vcral", |
| "ref_id": null |
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| ], |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "Creating new translation rules as well as refining existing ones have In'OVen to lm cxtr(~mely difficult 'I'h(-` aim ()f this work is to mak(! AUI.'-J/I,;'s tnmsla-(.ion rubes less costly and more rcliabh-` through tim us(! ()t' inductive machi,l(' h'a,',lin/,; techni(lueS. Car(!ful examinati,)n (Tf th(, mamml pr(7(:(~ss wlfich has been t271lmv,'d so far by Al;l '-,l/l';'s (~Xl) erts fin\" Imihling t:ranslati(m ruDs revc'ids that m(Tst of th(.' efl',n't is spent on figuring out the (:onditi(m part of the rules (that is, the 3apanesl~ i(att(~rns). Ther(~fore, we prol)OSC th(; (is(.' of indu(:tiv(~ machine learning algorithms t(( h~mn these conditions fi'onl examph~s of Japanese sentences and their English translations. Under this machine l('arning approach, the user is r(qi(wed from exph)ring th(! hug(: space of alt(~rmttives sl(e/hc, has to con.sider wh(m c(mstrnctinl,; translation rules manually from scratch-a job whi(:h only ext(msiv(!ly train(!d eXlT(wts can perf(n'm. Th(' task is now tin'ned into a s('ar('h tl)r s()m(~ r('as(Tnahh-` rules that explain t.lm given training cxamlTles , whbrc the search is han(lh-`d aut(mmti('ally by a learning algorithm. This not only sltves the tlser~s tiltl(}~ hilt idso lltakes it untle(:t!ssary for the user to be an expert of the AUI'-J/E system. Mor(~ver, this approa(:h sigmticantly reduces the \"subjectivity\" of the rules since the interwmtion of hmnlm exI)erts is minimized. This is tmrticularly important because tile iHllllense Illllllb(w of translation rules (currently over 10,000) requires employing a team of experts over an extended l)eriod of tim(!.", |
| "cite_spans": [ |
| { |
| "start": 365, |
| "end": 381, |
| "text": "'-,l/l';'s (~Xl)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "Two learning methods are investigated in this i ml)er. Ext)eriments show that the rnles learned by these methods are very close to the rules mmmally COmliosed by hlllIt}tll experts. Ill Hl(Ist cases~ givell a reasonabh~ mtmber of training examph~s, th(! employed methods are able to find rules that are more than 90% accurate when compared to the mamutlly COnlI)OSed miles.", |
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| "section": "Introduction", |
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| "text": "The rest of this document is organized as ti)llows. We begin in Section 2 by it brief overview of the AUI'-J/E Japanese-l.;nglish translation system. In Section 3, we discuss some of the 1)rol)lems that arise when the translation rules of ALT-J/E are composed manually })y }roman experts. Then, we t)ropose in Section 4 an alternative approach based on machine learning techniques. In Section 5, we describe the inductive learning methods used, followed by an experimental ewfluation of these methods in Section 6. Fimdly, conclusion remarks are stated in Section 7.", |
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| "section": "Introduction", |
| "sec_num": "1" |
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| "text": "ALT-.I/E, the Automatic Language Trlmslator: Japanese to English, is one of the most &dvitll(:(}d and well-recognized systems for translating ,htpanese to English. It is the largest such system in terms of the iunount of knowledge it compris(~s. In this work, we are concerned with the li)llowing components o[' the ALT-J/E system: 1. The Semantic lliera.rchy, 2. The Semantic Dictionary, and 3. Tile Translation l{ules.", |
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| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "We briefly describe each of these COmln)nents below.", |
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| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
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| "text": "For more details al)out the AI,T-.I/E system, we refer the reader to [lkehara et M. 1989 [lkehara et M. , Ikehara et al. 1990 [lkehara et M. , ikehara et al. 1991 .", |
| "cite_spans": [ |
| { |
| "start": 69, |
| "end": 88, |
| "text": "[lkehara et M. 1989", |
| "ref_id": "BIBREF2" |
| }, |
| { |
| "start": 89, |
| "end": 125, |
| "text": "[lkehara et M. , Ikehara et al. 1990", |
| "ref_id": null |
| }, |
| { |
| "start": 126, |
| "end": 162, |
| "text": "[lkehara et M. , ikehara et al. 1991", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
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| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "As shown in l\"igam~ 1, the Semantic ltierarchy is it SOFt of colt(:el)t t}l(?SltllrtlS represented its it l;l'(?e structure in which each node is called a .SC'IIta'tttiC categolw, or a (:atego'l~9 R)r siml)licity. Edges in this structure represent \"is-a\" relations am(rag the categories. For example, \"Agents\" and \"P(!ople\" (see Figure 1 ) are both categories. Tile edge between these two (:ategories indicates that any instance of \"l)eoph~ '' is also an instance of \"Agents\". The current version of ALT-.l/E's Semlmtic llierarchy is :12 levels (let, I) and has about 3000 nodes. The Semantic Dictionary maps (~it(:h .]~4pall('.sC IIOtlll to its aI)prol)riate SeItlalltic cRtcgories. For example, the Selilalltic D!ctionary states that the noun )~!:~ (niwatori), which meahs \"chicken\" OF \"h011\" ill English, is an instance of the categories \"Meat\" and \"Birds\".", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 329, |
| "end": 337, |
| "text": "Figure 1", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
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| "text": "The Translation Rules in AUI?-J/E associate Japanese patterns with English patterns. Currently, ALT-J/E uses roughly 10,000 of these rules.' As Figure 2 shows, each translation rule has a .]apanese frettern its its left-hand side and all English pattern as its right-hand side. For example, the first rule in this figure basically sltys that if the ,Japanese verb in a sentence is ~J'~ < (yaku), its subj('(:t is an instance of \"l)eople '', and its ol)ject is an instance of \"lh'ead\" or \"Cake\", then the following English pattern is to be llS(?d:", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 141, |
| "end": 152, |
| "text": "As Figure 2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "Sub.jeer \"l)ake\" Obj(!ct.", |
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| "text": "Note that in this (:~e the Japan(!se verb ~y~ ((yaku) is transhtted into the English verb 'q)akc'\". This slune .]aI)anes(! yet'l) cait also be translated into the English verbs \"roast\", \"broil\", \"crenmte\" or \"burn\", dependlug on the context. These (:~Lses axe }landled by the fore\" other rules given in Figure 2 .", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 303, |
| "end": 311, |
| "text": "Figure 2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "Translation rules are meant only to handle basic sentences that contain just a single .]itl)a.ltt.'se ver}). Such sentences are called \"simple selitellCeS. ''2 '[l'o translate a comlllex sentence, M;]'-,I/E does various ldnds of pre-and post-proc(~ssing, l/oughly speaking, the given complex sentence is first broken into a collection of simple sentences in the we-processing phase. Then, the English translations of these are combined together in the post-processing t)}u~se to give the final translation of the complex sentence.", |
| "cite_spans": [], |
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| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "To translate a simple sentence, AI:I'-J/E looks for tile most ai)I)roi)ria.te translation rule to use. Based on the VOl'b of the sentence, the system considers ius candidates all those tra.nslation rules that have this verb on their left-hand side. 'l'he English pattern of the rule, whose JaI)imese pattern matches the s0Iitell(:(! })est is th(!ii osod to generate the desired English translation.", |
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| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "As shown in Figm'e 2, the ,Ial)anese patterns are exln'essed using th(, wu'iM)les NI, N~,..., etc., which r(!][)H}s(}llt variollS COIllp()lleIltS of it Ja, pallese S(~Ilt(!llCe~ such as the subject, the ob.iect , et(:. :l The \"degree", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "O[ llilttchillg ~ ])otw(R!II it ,]ltl)alles(.' [liltt(!l'lI itlld it Sl~ll-", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "fence is based on how well the values o[' these vltriables for the given sentence match those categories required by the Japanese pattern. 'Fhe Semantic Dictin fact. AUI-J/E has three dith'rel,t kinds of translation HI.s: (i) the senlauti(' pal teru transfer rules (ront~,hly 10,000 l'uh,s). (it) the idiomatic expression tl'itli~.fer l'lll(.s (/i])Oltt 5.000 rules), and (iii) the p, en,.ral trallsfer rllh,s. We lINt ~ the lt'Hll \"'Tl'~illSliitii)ll l{llh.s\" 11t,1\"(, Io l'(,fel' to I]le .Siqllilliti( l)itttUllt trailsti,r rules. These form the majority of the rulos, alld they are the most fl'equently used by .kUI'-J/E. ~'lhe I(,i'lli \"'siml)le S(~lllt,llC( ,'\" iS it (lilei't translalitm of IgS~ (taulmn) in .lal);UleSe.", |
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| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": ":l']o be precis...]al)iil|~,s(, NI'llI,'II('t'N ill't* I|SllaIIv ])/tl'sed illIO a set ol (Olnlmn~mts (('ailed ~I -I{'}~ ~ -~, E -t~, etc.) that iIl'e quite di|felt'll! froln those used in English. Using \"'sul)j(.cI\" and \"'ob.i('ct'\" ]1(~1( ' is ouly lilt'Hilt to Cits(' lhe discussion fin' English l'ell (I(TS. ", |
| "cite_spans": [], |
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| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": ",V I A'I (,'3ubj) ~ \"l',',,l}h\"\" l'2-\\'erh = \" hroil\" N~ (()bj) -2 \"'l'T,h\" ' } I I ' ' S t ' m I I ( } I ' ' 1 ` I ( ) I } I i ~ 'X\" e 11\" 'I'IIEN .]-Verl} = \":[]~( (yaku)\" Subj = A'I N~ (Sub j} ~ ' A~,emn' E-Verb = \" {'rmnat{, \" N.., (()hi)", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": ": \"I'e{q}h:' .r \"Animals\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "()hi = N., ll.' TtlEN .J-V'erl)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "= \"~l~( (yaku)\" Suhj = .%'1 Art (~ul)])", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": ":: \".-\\.KunD,'\" m \"'Ma{hin,'s\" I~-\\,ql) 2: \" bulu \" N2 (()bj)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": ":c \"'Plac{'\u00a3\" or \"Obj,, ~\u00a3' el ()hi = .V~ \"l ,{IC~it i(lll~, \"\" l,'i~me 2: 'i'ranMatien rules f'(w t:he ,hq}an~.~e v~'rh f/t! < (yaku). 'l'he~e exl)('.rts. \" ~7i \" hl(li(:;tt,(!s \"an ill,taM(:(' of\".", |
| "cite_spans": [], |
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| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "rule~ are composed mammlly }}3' lmman tionary is used during the matching process to determine whether or not a given noun is an instance of a certain category.", |
| "cite_spans": [], |
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| "section": "ALT-J/E: A Brief Overview", |
| "sec_num": "2" |
| }, |
| { |
| "text": "Approach \"1)ranslation rules in the AI,T-,I/I~ system have so far been composed manually 1)y hunmn (!xl)erts. flowever, due to the high cost-1)er-ruh.' , and b(~(:aus(~ of the huge nmnlmr of translation rules needed fl)r AL'I'-,]/I); to carry out ;t reas()nabl(.' transhttion job, the manual apI)roach hms been conchided by the d(~veloI)ers of AUI'-J/I'~ to be impracticld. In particular, the l'(,lh)wing l)roblems have been wported:", |
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| "eq_spans": [], |
| "section": "Shortcomings of the Manual", |
| "sec_num": "3" |
| }, |
| { |
| "text": "\u2022 lhiilding and mmntaining the translation rules require *t greltt deal of expertise. \"1\"o qualify tin\" this task, skillflfl exI~erts are required not only to master both aal)anese and l!;liglish, Init also t() \u2022 One. of the problems fitting the design('rs of A1;I'-J/l~: is the refinement of the Smnantic lli(!rarchy.", |
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| "section": "Shortcomings of the Manual", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Whenever this structure is altered, the translation rules mnst also t)e revised to r(qh*(:t the change. Such revision is extr(~mely troubh~sonu., and error-prone if it is don(; mamlally. The goal of the pr(!sent work is to learn what we call \"partial translation rules\". A partial translation rule consisls ()l\" the left-hand side along with the English verb of the right-hand side of a translation rule. hi other words, the otlly diflin'en(:e between it transla.tion rul(.' and at partial translation ruh j is that the latter has only an I']nglish verl) rather than it full English patt0rn its its right-hand side.", |
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| "section": "Shortcomings of the Manual", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Constructing a partial translation rule is the most ditllcult part of constructing a. tl'anslati(m rule. lnd(~e(l, t;/ll'liillg it l)itrtial Fill{! into a comlil(!te one is a relatiw~ly easy t;ask that can Im done by a human operator with moderate knowh!dge of English and ,J al)~Ul(!Se.", |
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| "section": "A Machine Learning Approach", |
| "sec_num": "4" |
| }, |
| { |
| "text": "In this work, we investigate two dift'erent inductiw, l('arning algorithms. Before talking about these algorithms, we will first IIiMc.e the learning task more precise, alid shed some light Oil the diftlculties that distinguish it from other previously studied learning tasks.", |
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| "section": "Learning Task and Methods", |
| "sec_num": "5" |
| }, |
| { |
| "text": "The .iol) of a learning algorithm in our setting is to construct partial translation rules, l,'or a given ,lapan(~s(! verb ,l-vcr'b and a l)ossil)le English transhltion l,?-vcrbi of that verb, the MgorMlm has to llnd the npln'ol~riate condition(s) that should hoM in the ('i)litoxt ill Ol'dOr ti) Illlt 1) ,]-'O,f~ 'l'l) to E-VC.'tq)i.", |
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| { |
| "start": 315, |
| "end": 320, |
| "text": "'l'l)", |
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| "eq_spans": [], |
| "section": "Tile Learning Task", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "As an exmnlfl(! , consider the ,lapanese verb /!E 5 (tsukau). This verb corresponds to the English verbs \"use\", \"spend\" and %ml)loy\". The c}loice aniong these IDn.t~lish verbs del)(mds mostly on tim o}@ct of the sentence, l,'or example, if the object is mi instance of \"Asset\" or \"Time\", then \"spend\" is itpl)ropriate. Thus, it rough rule for mapping \u00a2< 5 (tsukau) to \"Slmnd\" may look like lly looking np the Semantic l)icti{)nary of AI/I'-.I/IQ the i}ossibh~ semanti{: catep;ories ft}r (mjyo are \"Noble Person\", \"Daughter\" anti \"Female\", antt thosP for kane are \"Asset\", \"Metal\", \"l)ay\" and \"M*'dal\". Thus, this example is tiredly giwm to the learning alp;tn'ithm in the folh}wing fl)rm: 1,:-v,,,I,) whol'e e~/ch Ni reI)resents a COlllp(}II(!II{. of the S(HltelICtT (sul}ject, ol)ject, etc.), mitt ea{:h ai,bi, and ci is a senlantic category.", |
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| "ref_spans": [ |
| { |
| "start": 689, |
| "end": 700, |
| "text": "1,:-v,,,I,)", |
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| } |
| ], |
| "eq_spans": [], |
| "section": "Tile Learning Task", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "l\u00a5om the viewpoint of machine learning r('s{!ar{:h, the al)t)vt~ h'.arning task is inter{~sting/(:hall('nl;in~: from two l}erspet:tives: ~, Iluge~ amount of backgrom,{l knowledge:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Tile Learning Task", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "'lb I}e apl)roI}riate for our learning task, the learning algorithm must efl'{~ctively utiliz{~ AI,T-J/E's large Semantic lIierarchy. This requiremerit of being {'al}abk' t)f t~xl}l()iting such a hug{' amount of lm{:kgrt}und knt)wh~tlgt' (lisqualilics most of the known inductivt~ learning algorithms froln dirct:tly l)eing nsed ill our domain.", |
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| "eq_spans": [], |
| "section": "Tile Learning Task", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "\u00ae Ambiguity of the training examI)h~s: Unlike mr}st known learning doinains, tim trainint~ exa.mph,s in tmr setting (as givml in Et I. (l)) are ambiguous in the sense that cat:h (ll the varial)h's (SUII.IECT, OILIECT, etC,) iS assignt~tl multipl(' wdues rltther than a single value, l\"(){:usinl~ t}tl the rehwant wdu{!s (that is, the va]ue~; tha.t contrilmted to the chtlice of the t,;nplish v(!rb) is an extlTit challenge to the l(!ill'Ii(!r ill ()Ill' (l{}IIlaill.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Tile Learning Task", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "To deal with th(' above learning l)l'{)bh!m, w{! in-vestigate{l two al)I)roat:hes. One is based {m a tl~e()retical algorithm introdnc(,d by l lm~ssh,r fin\" learnint~ internal disjunctive conceI)ts, and the (,thor (m tht, wdl-known ll)3 alg(}rithm t)f QuiMan.", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Tile Learning Task", |
| "sec_num": "5.1" |
| }, |
| { |
| "text": "Haussler's algorithm for learning internal disjmlctiw', exl)res:dons hi ()lit t[l'S|, al}I)roach, we relwt'stml the c(m(lil.i(ms (}f the h~arned partial translati{m rules as i~h:rTml disj'uncli'vc c.:lPp't'cssio'tts, an{1 mnI}h) y an all;or(tirol given l)y llaussltw for learning {:oncel)ts exprbssed in this syntax, lhulssh!r's alg(}rithm enjt}ys many adwm-taD's. ]:irst, it has lwen analytically t}rt}vtm to l}e (luite tqficient both in terms of time and the munt)t'r (if ('Xaml)h's nt'(,detl f{), learninp;. S{!ct)ntl, tlw aIp;orithnl is Cal}al)le {)f exl}licitly utilizing the I)a(:k-grtmn{I kn(iwledgt~ rt'pr{'sentt~d ]}y tht~ Semantic llier-;U't'lly. Mt)r('{tvt!r, l.]le latlg\u00a3ttage used ]}y hlllrla.l| eXl){!rl.s It} t't)nslruct AI:I'-,I/E's rules is quite similar t,t} in((!rhal disjunctivt~ expr{~ssit)ns, suggesting the aI)prol}riateness ()f this alpiocithul's bias. 1 laussler's alporithm, on the other hand, suflbrs the iml)ortant sht)rtctmfing (within ()ur setting) that it is not Cal}abl{! t}f It,art> ing from ambiguous examl}h's. In orthq\" t,o I)e able t() use the algt}rit.hm for our tav~k, the atnl)ip;uity has It} be exl)licitly r('m(wt'(1 fr{}m all the training (~xanll)lt's. ()f c(,m'se, this al}i)rtmch is not desirable I)t'lraust~ it r{xltlil'{!5; s(}lllO ilti{,rvt'ltti{)ll t)y a, hllllliIll eXl)tWt im(l ])(,{'ause tht'rt~ are st) {,31aratd.t'('s that tlisam})ip~ual.itm iS doll(! ill [I l)crfi~ct mamm]'.", |
| "cite_spans": [ |
| { |
| "start": 183, |
| "end": 216, |
| "text": "disj'uncli'vc c.:lPp't'cssio'tts,", |
| "ref_id": null |
| }, |
| { |
| "start": 217, |
| "end": 228, |
| "text": "an{1 mnI}h)", |
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| "section": "5.2", |
| "sec_num": null |
| }, |
| { |
| "text": "Quinlan's 11)3", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "5.3", |
| "sec_num": null |
| }, |
| { |
| "text": "()ur st!cond ai)l)roach is based on th(~ 11)3 algorithm intrtMuced I)y Quinlan in [Quinlan 198(; ] . As il~ is, 11)3 is ilot al}lc ~ to utilize the 1)ackgrt)lmd knowledge of (mr domain, nor is it capable of dealing with ambiguous trahlhlg examplt!s of the form given by lCt I. (1). It. b; (:h!arly inal)l}rtq)riat:t! to {xt!al, NI, ~V2\"\" its multivahwd variabh's, which is the tilt)st, c()tlllll{)ll w}/y o[ using I1)3. This is because of the hug(.' munbm\" of wdllt'S thest,, variables (:till Lake, ilIld IllS() I)(!CILIIS(~ V,'t! lit!(!({ to ext)loit the Ba{:kgromM knowh!dge represented by the Semantic 1 Ih!rarchy.", |
| "cite_spans": [ |
| { |
| "start": 82, |
| "end": 96, |
| "text": "[Quinlan 198(;", |
| "ref_id": null |
| }, |
| { |
| "start": 97, |
| "end": 98, |
| "text": "]", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "5.3", |
| "sec_num": null |
| }, |
| { |
| "text": "To bt! ablt~ to use 11)3 ill {}llr d()lllllill~ We I.l'}tllS-ft}rm the training exanq)les into a new representatitm thai. can ])l! handled by 11)3. The tla.nsfornial.ion wt! ln'Ol)t)se is (lime in a way such that the ]'elevant inf(}r-III;l[.i()II fr()Ill tll(~ t.ho StTIIla.llt.ic lli(!rar{:hy art! inchM{!d in the newly rel}rt~s('ntt'd eXaml}h~s, anti, id, tilt! HD.III(! (lille, these nt'wly rt'l}restmted eXaml}l('s still r{qlect the amBiguily l}rt's('nt iu tim t)rit~inal (!Xaml)l('s. oxami)h'~ imo a ut'w pair (V, I'J-Vcrl, ) wh('re 1' is a vt't:tt}r of bits ea(h I'{'I)I'{!S(!I{LiIII!~ the O/ll('t)lllt~ t)f t.h{! corrcsl)ouding t~at.m'(\" for t.he given training eXaml)le.", |
| "cite_spans": [ |
| { |
| "start": 515, |
| "end": 530, |
| "text": "(V, I'J-Vcrl, )", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "5.3", |
| "sec_num": null |
| }, |
| { |
| "text": "the new pMrs (V, I '2-Verb ) in{:lude all the necessary l)aekgTom,d knowledge obtMn(xl form ttu., Semantic ltierarchy, and also reflect the ambigafity of the origimd trldning examt}les. In uther words, the above transformation can i}e seen as \"cOral}fling\" the information of the original ambignous training examph.'s along with the necessary parts of the Semantic llierarchy into a format that is ready to be proce~sscd 1}y ii)3 (or in fact, by many other feature-t}ased learning algorittmls).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Given the above definition of the binary features,", |
| "sec_num": null |
| }, |
| { |
| "text": "Note that if we create a featme fur every semantic category c and every sentence COmllonent Ni, then the total number of features will become inti.'asiblv large (Inany thousands), llowe.ver, what we need is only to consider those categories that apl}eared in the training data, and their ancestors (the set A above).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Given the above definition of the binary features,", |
| "sec_num": null |
| }, |
| { |
| "text": "In our experiments, this results in a reasomfl}le ram> ber of features (one to two hundred). This is 1}ecause the numl}er of examples is limited and also t)ecause of the rather \"tilted\" distribution of what categories can naturMly at}I}ear as a certain (:OlIll}Otlellt of it Selltenee for a given verb. (Eg. the object of the verl} f;2 ~3\" (nomu), which roughly means to \"drink\", can not be just mlything!) The most important a(lvmltage of the al}ove approach is that it cmt be applied to alnbiguous training examl}les as they are, without the need to remove the mnbiguity explicitly as wc did with Ilaussler's algorithm. Another adwmtage of using ID3 is that we do not need to break our learning task into binary class learning problems since ID3 is caI}ablc of Mu'ning multi-class learning concepts.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Given the above definition of the binary features,", |
| "sec_num": null |
| }, |
| { |
| "text": "The goad of tile experiments reI}orted here is to evaluate the qmdity of the partiad translation rules learned by the two h.~m'ning methods we have just descril}ed. The comi}arison includes the folh}wing three settings:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Experimental Work", |
| "sec_num": "6" |
| }, |
| { |
| "text": "1. Using llaussler's algorithm to learn fr{}ill training examl}les ~ffter removing the mnl)igulty.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Experimental Work", |
| "sec_num": "6" |
| }, |
| { |
| "text": "2. Using ID3 to h;arn from training examl)les after removing the ambiguity atnd performing the transformation given in the Subsection 5.3.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Experimental Work", |
| "sec_num": "6" |
| }, |
| { |
| "text": "3. Using ID3 to learn from tnfining examI}les after performing the transfi)rmation given ill tile Subsection 5.3, trot without removing the. ambiguity.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Experimental Work", |
| "sec_num": "6" |
| }, |
| { |
| "text": "In a sense, the first setting rellresents the lmst we can do in the absence of the ambiguity since llmlssler's al-goritl}m does at good job in exi)loiting the baekgT{mnd knowledge fi-om the Selnanti{: Ilierarchy. Comparing Setting 2 with Setting 1 tells us how successfifl our transformati{m of the training examl}les is in letting 1D3 make use of the available I}ackground knowledge. Fimdly, comparing Setting 3 with Settir,g 2 tells ns how successful our transhn'mation is in letting 1133 learn directly froin amt)igalous training examl)les. The experiments were done tbr six ditl'erent .lapanese ver/}s. '.['able 1 shows a list of these verbs, along with the II/lltl})er of training eKauli])h!s llsed, and the a{:cura{:y levels obtained by each meth{}d. In the table, \"tlausslcr\", \"ID3 NA\" and \"11)3 A\" de.note Setting 1, Setting 2 and Setting 3, resl}e{:tively. The a(:curacy was esthnated using the leaLvt>olle-{}llt {:rosswflidation meth{}d '| , m,d assuming that the rules {:{)m-I)osed rnamutlly by human experts are t}erfect (that is, we are measuring how close tim learned rules are to those {:Omllosed mmmally).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Experimental Work", |
| "sec_num": "6" |
| }, |
| { |
| "text": "The i)erti}rmanee levels of both lhmssler's alg()rithm and ID3 when learning from unambiguous examples are quite similar in Sl)ite of the fact that each algorithm implements a different bias and has a completely diftin'ent way {}f' exl}loiting the background knowledge. Coml}aring tim l}erformance of ID3 in the two cases of leil.rIlillg froI [l itIIl]}ig/l(}llS &ll([ IllHlI[l- I)iguous examl}les , ambiguity is not harntful t(} ll)3's l}erforman(:e in most cases. In fact, for some of the verbs, the t}erforlIl~tn{:e is evelk ])etter when aml)iguity is present. This suggests that the apl}roach we have chosen to de.al with ambiguity is effective for our task, and tl,at ext}licit retll{}vitl o[ ambiguity is not an attractive strategy sim:e it is not easy to {t(}, and since it does not greatly improve the a(:{:m'aey anyway.", |
| "cite_spans": [ |
| { |
| "start": 343, |
| "end": 378, |
| "text": "[l itIIl]}ig/l(}llS &ll([ IllHlI[l-", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Experimental Work", |
| "sec_num": "6" |
| }, |
| { |
| "text": "The most important ll(}int here is that the ol}served a{:cura{:y of both the. 11)3 a.lgorithm aim llaussler's algorithm is satisfactorily high overa!.l in spite of the limited mmfl}er of the training examl}k's used. Such a high level of at(:curat(:y str{mgly indicates that the use of these algo,'ithms will provide significant aid in the c{}l,struction of AI/.I'-J/E's trmMati{}]t rules.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Experimental Work", |
| "sec_num": "6" |
| }, |
| { |
| "text": "This paper reported our work towaMs the acquisiti(m of,hqmnese-lCmglish translation rules through the use of inductive machine learning techniques. Two approaches were investigated. The first aplmmch ix based on a. theoreticMly-f(mnded algorithm given by l lmlssler fl}r h~arning internal disjunctive eoncel)tS. This algorithm haLs the advantage that it is tailored to utilize background knowledge, of the kind availabh~ in our domain. We f{nmd, howeww, no obvious way to make this algorithm learn directly t'mm ambignous training examples, and thus, anlbiguity wlm explic-. itly removed from the training exmnph~s in order to use this algorithm. Om' second apl)roach ix based on the IDa algorithm. As it is, i1)3 is not Mile to utilize the background knowledge of our domain, nor is it capable of dealing with ambiguous training exam--I b'Xallll)h, s ill't' vxchldell frOlll the tl'aillillg st,t Ollt * ~l [ il IilllO. :[ho i'llI(, hqllllt'd [iOlll I] Experimental results on six ,lapanese verbs. Nulnbm's show the accuracy trot-cent, estinmted using tit(.' leave-one-out cross-validation method. 11)3 NA indicates using 11)3 wit.h the ambiguity removed fi'oIlI the training examl)les. I1)3 A iudicates using 1I)3 to learn from aml@~uous training eXaml)les. n;;..g A,,,,,a;i;;,; '/ l)]es. We gaY(-`, }towevtw~ air (!a-qy Way to \"(:()m])il(\u00a2' the relewmt backgrouiM knowh!dge along with th(! ambignous training examl)h!s into a modilied set o[ training examph!s on which w,! were abh! to directly run 11)3. Experiments comparing these approach\u00a2,s showed that the rules learned using the second ap preach with the ambiguity present in the training cx-3.Ittpl(!s are ahttost as 3.ccltt*~ttt! ils those ()})tltill(!d fl'ollI arnlfignity-free examples using llaussh'r's alg(n'ithnL Ow.'rall, our experiments sho~ed that using Iliachine learning techniques yiehls ruh!s that are highly itct:llrltte (:otllpared to the ttuttntally created rules. These results suggest that exploiting the reported inductiw. \u2022 lem'ning techniques will significantly accehq'ate the construction process of AIJI'-J/E's translation ruh.'s. Currently, the reported learning aplnoachos are I)eing inchlded in at semi-imtonmtic knowledge aC(luisition tool to be ttsc(l ill the actual (leveh)im,ont of the AUI'-J/F system.", |
| "cite_spans": [ |
| { |
| "start": 907, |
| "end": 952, |
| "text": "[ il IilllO. :[ho i'llI(, hqllllt'd [iOlll I]", |
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| } |
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| "ref_spans": [ |
| { |
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| "text": "A,,,,,a;i;;,; '/", |
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| "eq_spans": [], |
| "section": "Conclusion", |
| "sec_num": "7" |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "\\Ve wish to thank l)r. S. lkehara for his COlltiitllOllS (!ttc()ltrilg{~Itlcllt. This work W~LS done while the first author was spending a I)()stdoctoral yem. at NTT. lle Mso thanks King l\"ahd Un-w~rsity of Petrohmm and Minerals, Saudi Arabia, for their support.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Acknowledgelnent:", |
| "sec_num": null |
| } |
| ], |
| "bib_entries": { |
| "BIBREF0": { |
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| "ref_entries": { |
| "FIGREF0": { |
| "type_str": "figure", |
| "num": null, |
| "text": "(Tttl(m(~ck are delinit(~ly need(:d. ()he major (:(Tmi)on(mt of this system is its huge (:oll('(:tion of trm~sl,~ti(m. t'ltlcs. Each of these rules associates a .]alTmlCSC s(,[lten('(' I);d,t(Tn with an aI)I)roI)riat(-` l,'mglish pattern. To translat(: a Japanese s(~ltt('.iic( ~, into l';nglish, AI;I'-,I/I'; hiol~s lbr the rul(~ whose ,]almli(!s(! i);ttt(wil llHttch(!N t}l(! S(!ILt(!II('(! best, and then uses the English ])~-ttt(~l'l| O[' thatt rule for translation.", |
| "uris": null |
| }, |
| "FIGREF1": { |
| "type_str": "figure", |
| "num": null, |
| "text": "~ltid tiHl(~-COllSll(liill~ l)(?(:a/iSC thcsc t~(.sks r(~(l(lil'(! col(~ sidering a huge space ()f p(Tssibh~ comlTimtti(ms (rules in AI;['-,I/E at(! (~xpr(.'ssed in terms of as much as 3000 \"semantic categorieF'). The high costs involved make the mmmal creation of ALT-.I/E's translation rules impractical, hMeed, in si)ite of the w~st mnount ()f r(,sources sp,mt ,)n building th(-` current ruh!s of A LT-J/I!', faults in these rules are still d(~tected fi'om time t() tim(.', making system l[(kl.illt(!Ilatiic(~ it c(mtinu-Oils 1\"(!(I 11] F(!(ll(!l It.", |
| "uris": null |
| }, |
| "FIGREF2": { |
| "type_str": "figure", |
| "num": null, |
| "text": "X~[ HLIRlar/N ~ Old / YOLInO / ,, Male / fem~41(~ ~\"'-~Mal\u00b0 / Female~-~ ~ Male \",,',--,,. ~ f:emalo l,'igur(~ 1: q'h(, upper h!v(!ls of th{! Semantic lli{war(:hy in AI:I'-,I/I'2.", |
| "uris": null |
| }, |
| "FIGREF3": { |
| "type_str": "figure", |
| "num": null, |
| "text": "b('. flflly fiuniliar with Al;I'-J/l';'s large S(~lnanti(: llierarchy and to understand the overall l)l'()(:(.'ss of the system. Such qualifications are costly and involve extensive training. . In spite of the wmt am(rant of resourc(~s spent on tmilding the current ruh!s of AI2F-.III'; by human exports, faults are still detected from time to tinm, Inal\u00a2ing the malnt(!ilance of th(; system ~t ('oiltillllOliS r(~(|ll]r(!Iil(}ilt. \u00ae The translaf.ion rules are not qnite coucrch: and vary dep(mding on the exI)ert. Rules (:onstructed by Oil('. oxpcl't ~-tl' (~ 11(){; (}asy for [tiloth(H\" (}XpCl'[, t() understand and modify. This makes the. maintcnine(! process ll)ore difficult and ii'lltkl~s it hard to substitute an expert by another, An important o/)jective is to tmild sI)ecialized versions of ALq'-.} /I,; to be used in specitic al)pli(:ai;ion domnins. 'l?he Illttllllltl ai)proach is o/)viously unrealistic since it illvolveS Inor(! irainiug of the human experts with r('sp(!(:t I;() the l;arg(!f, application doina.in, alld I)(~(-itllS0 this l)rocess hm; to |)e repeated for (!v0ry new d()lHiliil.", |
| "uris": null |
| }, |
| "FIGREF4": { |
| "type_str": "figure", |
| "num": null, |
| "text": "i~Rn = spend. \\VO S(!('I'7. to ]Oitl'll this kind of l'lll(!s frolll exatl~ll)lt!s of ,hil)anese senti.mces and their I:;nglish translations, such as the following pair: { . I'i&':~:~= ~:{~L ' 5, Tim l}rincess sp(!n(Is mt)lmy ). After parsing (which is carrie{l trot by AI,T-J/Iq's parser), the. above exanq)le gives the ft}llowing l)ail': ( [ J-\\~:,u = ~5 . ~;tuuEc'r = mtj>,). OBJECT = k;(Iw ], E-VERLI =~ Sl)eltd ).", |
| "uris": null |
| }, |
| "FIGREF5": { |
| "type_str": "figure", |
| "num": null, |
| "text": "[ .~UILII,:t'T ~ { Noble Person, l)an~ht,,r. Fen.de }. ()llJE('q ~ { Ass01, iXl,'tal, l)ay. Medal }] . I\".-VEItB == Slmltd ), where N :~ , %\" indicates I;hat t}m senl:(m{'t' c()mI)(}n{mt N is an instant:e, of each category s (2 ,5'. '['lw p;('n(wal fin'mat t)t\" the training examI)h's is as f{)ll{iw~< ([ N, ~ {a,,a2,...}, & -= {b,,b.~,...},... (~) N. < {,,~,,,.,, ...}],", |
| "uris": null |
| }, |
| "FIGREF6": { |
| "type_str": "figure", |
| "num": null, |
| "text": "()Ill\" t.FilllSf()I'IIID.tit)ll lIl{q;hotl is d(~scril)ed as follt)ws: L('I. A I}{y tlw set ()f all the catetv)rit's (hilt alIl)tmrc(l in the (raisin(; exanll)h's , and t,heir ancesl.t}rs. I:or {wery c (! :1, w(! (It!lint! it bhml'y f(!atui'{~ a.s ;t tt!sI; t)[ th{! t(ll'Ill Is Ni an instance of (2 For it training {!Xmnl)le ([N, ~-.,fi,... Ni ~ Si,.-. N,, -S,,], l'LVcr'b), we let the t)utctmie of the abt}ve test I}e t't'm', if and only if tiwrt! exists some s ~ Si such that s is ;Ill an{'e:~t{w of ,\" in the }~{'nlanlic I1itwar{:hv, ()r (: itself. Using {hi,s{, features, we c(mvtwt each t}f lhe {raininl,;", |
| "uris": null |
| }, |
| "FIGREF7": { |
| "type_str": "figure", |
| "num": null, |
| "text": "lo l'('sl of ~hl, I'Xalltllllt's is thlqt IINl'd to l}rPdict the {'lass o[ tilt, l'lqllOXl,d eX;tllllllt.. This ',',';Is I'{'I}{'atod for all lhe (,Xilllll}lus. illlll the ]){,l{'(,lllf/l~},t , o[ ('{}IT{'(I (htssilicatilllt iv} l't'l}Ol't i'd.", |
| "uris": null |
| }, |
| "TABREF0": { |
| "type_str": "table", |
| "num": null, |
| "text": "An attractive approa(:h to this l)robhmi is lto resort to inductive machine learning techniques to extract the desired translation rules fl'om examples of .laI)anesc sent(m(:(~s and their English translations. At tit(.' on> rent stage, how(wet, learning translation rules fully automatically from eXaml)les alone seems to lm too chalhmging. A more realistic goal is to minimize rathc'r than to totMly eliHlinat(~ the intervention of human exp('rts in the rifle aquisiti~m process.", |
| "content": "<table><tr><td>Thus,</td></tr><tr><td>OIll\" Cllrl'(?Ilt o1)jectiv(~ is to ('OllCOIltl';itt(~ 011 ~Ult.Olll~tt-</td></tr><tr><td>ing l;he niost ditlicult and tinl(>(:onsnlning parts of the</td></tr><tr><td>niallllal procedure.</td></tr></table>", |
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