| { |
| "paper_id": "C94-1015", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T12:48:38.239733Z" |
| }, |
| "title": "Constituent lloundary Parsing for Exanll)lo-lkised Maclhine Tr,'inslation", |
| "authors": [ |
| { |
| "first": "Osamu", |
| "middle": [], |
| "last": "Fui", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "ATR Interpreting Telocomnlunications Research Laboratories", |
| "institution": "", |
| "location": {} |
| }, |
| "email": "" |
| }, |
| { |
| "first": "[", |
| "middle": [], |
| "last": "Ida", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "ATR Interpreting Telocomnlunications Research Laboratories", |
| "institution": "", |
| "location": {} |
| }, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "This paper i)roposes an effective parsing nicthod for examlile-based machine transhltiOl~. In this method, an input string is parsed by the tOl)-down aplflication of linguistic patterns consisting ol variables and constituent boundaries. A constituent boundary is expressed by either a functional word or a l)art-of..speech bigram. When structural ambiguity occurs, the most plausible structure is selected usin b, tile total values of distance calculations in tile oxanll)le-basod Iraillework. Transfer-Driven Machine Translation (TDMT) achieves efficient aitd robust translation within the example-based framework by adopting this parsing method. Using bidirectional translation between Japanese and Vnglish> tile effectiveness of this method in TDMT is nlso shown.", |
| "pdf_parse": { |
| "paper_id": "C94-1015", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "This paper i)roposes an effective parsing nicthod for examlile-based machine transhltiOl~. In this method, an input string is parsed by the tOl)-down aplflication of linguistic patterns consisting ol variables and constituent boundaries. A constituent boundary is expressed by either a functional word or a l)art-of..speech bigram. When structural ambiguity occurs, the most plausible structure is selected usin b, tile total values of distance calculations in tile oxanll)le-basod Iraillework. Transfer-Driven Machine Translation (TDMT) achieves efficient aitd robust translation within the example-based framework by adopting this parsing method. Using bidirectional translation between Japanese and Vnglish> tile effectiveness of this method in TDMT is nlso shown.", |
| "cite_spans": [], |
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| "section": "Abstract", |
| "sec_num": null |
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| "body_text": [ |
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| "text": "1 Introduction I-xample-basod franieworks are increasingly being applied to machiilo translatioi/, since th0y c~.ill l)rovido efficient and robust processing (Nagao, 1984; Sate, 1991; Sumita, 1992; Furuse, 1992; Watanabe, 1992) . However, in order to make tilt best use o1 the a(.lv:.lnlages of an example-based fl'amcwork, it is essential to effectively integrate an example-based method anti source language analysis. Unfortunately, whcll all exainl)lebased nletiiod ix combined with a SOUFC0 lnnguago analysis inelhod having cOlnl)lox l~r~illilliflr rules, pulling a heavy load eli translalion, the advai/lai;os of lhe example-based franiowork iilay l)e ruined. To achieve efficient and robnst processing by the exanii)lc-basod framework, a lot of sttldies have beell nlado for the pui])ose of combining source lal!gtiage analysis with all example-based method, lind of efficiently covering the analyzed source langilllge strtiCttlro by me;illS of trailsfcr knowledge (Grishman, 1992; Jollcs, 1992; McLean, 1992; Manlyama, 1992 Manlyama, , 1993 Nirenburg 1993) .", |
| "cite_spans": [ |
| { |
| "start": 158, |
| "end": 171, |
| "text": "(Nagao, 1984;", |
| "ref_id": null |
| }, |
| { |
| "start": 172, |
| "end": 183, |
| "text": "Sate, 1991;", |
| "ref_id": null |
| }, |
| { |
| "start": 184, |
| "end": 197, |
| "text": "Sumita, 1992;", |
| "ref_id": "BIBREF13" |
| }, |
| { |
| "start": 198, |
| "end": 211, |
| "text": "Furuse, 1992;", |
| "ref_id": "BIBREF0" |
| }, |
| { |
| "start": 212, |
| "end": 227, |
| "text": "Watanabe, 1992)", |
| "ref_id": null |
| }, |
| { |
| "start": 971, |
| "end": 987, |
| "text": "(Grishman, 1992;", |
| "ref_id": null |
| }, |
| { |
| "start": 988, |
| "end": 1001, |
| "text": "Jollcs, 1992;", |
| "ref_id": null |
| }, |
| { |
| "start": 1002, |
| "end": 1015, |
| "text": "McLean, 1992;", |
| "ref_id": null |
| }, |
| { |
| "start": 1016, |
| "end": 1030, |
| "text": "Manlyama, 1992", |
| "ref_id": null |
| }, |
| { |
| "start": 1031, |
| "end": 1047, |
| "text": "Manlyama, , 1993", |
| "ref_id": null |
| }, |
| { |
| "start": 1048, |
| "end": 1063, |
| "text": "Nirenburg 1993)", |
| "ref_id": "BIBREF10" |
| } |
| ], |
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| "text": "One wily to reduce tilt load of source langua!,,c analysis ix to directly apply trallSl'cr knowledge to all input siring, which sinlultaneously executes both siruciinal parsing aiM transfer knowlc.dgo al)lHication through pattorll-il/atchii/g, l:'allerll-nlalchi~ig does liot rise grainillaticaI symbols such as \"Notlil Pliraso\", but uses surfi.ice words an(] non-granlmalical synlbols. Therefore, in patlern-matching, rule coml)otition is reduced, and linguistic structure is expressed in a simpler manner thall ill gramnmr-based parsing. Thus, pattern-nlatcifing achieves efficient 1)arsing. It is also useful in treating spoken language, which sometimes deviates from convcntion:ll grammar, while grammar-based p,'lrsing has difficulty treating ilnreslricle (l spoken I[ingllll,ge. This pal)Or proposes a constituom boundary parsing method based on paltorn-niatching, and shows its effeclivonoss for spoken langnago translation within the exaniple-I)asod framework. In otlr parsing method, aii inl)Ut string .is applied linguistic patterns e\u00d7pressing some linguistic constitticnts and their bonnds-lrios, in a top-down f:.tshion. \\Vhon structural anlbiguity occurs, tile most phlusi/)lo structure is selected rising the total vahios of dislanco calculations in tilt example-based lrs-Illiowork. Shico the description of a linguistic ps-ittern is sinlplo, it is easy to update by adding f0etlback.", |
| "cite_spans": [ |
| { |
| "start": 761, |
| "end": 784, |
| "text": "(l spoken I[ingllll,ge.", |
| "ref_id": null |
| } |
| ], |
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| "text": "A constiLuonl boundary ixusing method using nuitual illfoiillation i~ l)roposed in (M,'lgerlflan 1990 ). This method accouilts for the unrestricted lls-ltLlra] langtlage and is efficient, llowever, it tends to be illacctirate> and difficult, to ad(l feedback to, since it completely depends on st'ltistical information withoul, resort to a linguistic viewpoint. On the cont,ary> in order to achieve accurate parsing and Iransb'ition, our conslituent boundary parsing method implicitly incorporates grammatical information into p'ltterns, e.g. constituent boundary description by a i)art-of-sl)eech bigrani, and classification of i)ailerns according lo linb, uislic levels such s.ls simple sentence ,tlrld tlOtHI l)hrase.", |
| "cite_spans": [ |
| { |
| "start": 83, |
| "end": 101, |
| "text": "(M,'lgerlflan 1990", |
| "ref_id": null |
| } |
| ], |
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| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
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| "text": "Tlallsfer-Orivell Maehillo TranslatiOll (TI)MT) ( [:tlrtiso> 1992, 1994) uses tile COl/Stil.llont botlndary 1)a~sint ,, liielhod l)l'eSollto(l in this paper, as an alternative to glamliiar-based ali:.ilysis, aiKI lliakos the i)ost ilSe of the ex:lmplo-based framework. A bidirectional translation syslcnl between Jap,'lnesc lind English for dialogue sentences concerning international conference regislralions has been illlplenlented (Sobashima, 1994) . l~xperimonts with the systonl have shown ollr parsing iiicthod I() t~ effcctive.", |
| "cite_spans": [ |
| { |
| "start": 50, |
| "end": 72, |
| "text": "[:tlrtiso> 1992, 1994)", |
| "ref_id": null |
| }, |
| { |
| "start": 434, |
| "end": 451, |
| "text": "(Sobashima, 1994)", |
| "ref_id": "BIBREF12" |
| } |
| ], |
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| "text": "Section 2 defines patterns expressed by variables and con.<;liluont boundaries. Section 3 OXl)lains a method for derivin{, possible English structures. Soelion 4 explain'4 structural disanibi,gnaliOti using tlislanco calculations in Iho o\u00d7anilflo-b,'lsed framework. Section 5 exphlins an example of Japanese sent0nee analysis using our consliluont boundary parsing method> and Section 6 reports on the experimental resulLs.", |
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| "text": "A pattern represents meaningful units for linguistic structure and transfer in TDMT, and is defined as a sequence that consists of variables and synrbols representing constituent boundaries. A variable corresponds to some linguistic constituent, and a constituent boundary does not allow any two variables to be adjacent. A constituent boundary is expressed by either a functional word or a part-of-speech bigram marker l", |
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| "eq_spans": [], |
| "section": "Pattern", |
| "sec_num": "2" |
| }, |
| { |
| "text": "The explanations in this anti the subsequent two sections, use English sentence parsing. Table 1 shows tile English parts-of-speech, currently used in our English-to-Japanese TDMT system. This part-of-speech system does not necessarily agree with that of conventional grammar. In this part-of-st)eech system, a be-verb, auxiliary verb, preposition, conjtmction, deterntiner, and suffix, are classified into a functional word.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 89, |
| "end": 96, |
| "text": "Table 1", |
| "ref_id": "TABREF0" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Pattern", |
| "sec_num": "2" |
| }, |
| { |
| "text": "One problem with pattern descriptions using surface 1 In this paper, variables, actual words, and part-ofspeech abbreviations are expressed in calfital letters, italics, and gothic, respectively.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Constituent I)()ulldary marke,\" exl)ressed by a functional word", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "words is the necessity for a large number Of patterns. To snppress the nnnecessary patterns, the surface words in patterns are in principle restricted to functional words, which occur frequently, and which modify or relate content words 2. Fnr instance, the expression, \"go to the station\" is divided into two constituents \"go\" and \"the station\", and the l)reposition, \"to\" can be identified as a constituent boundary. Therefore, in parsing \"go to the station\", we use tile l)attem, \"X to Y \", which has two variables X and Y, and a constituent boundary, \"to.\" 2.3 Constituent I)oundary marker expressed by a pa,'t-nf-sl)eech hig,'anl The expression \"1 go\" can be divided into two constituents 'T' and \"go.\" But it has no surface word that divides tile expression into two constituents. In this case, a part-of-speech bigr,'un is used as a constituent boundary.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Constituent I)()ulldary marke,\" exl)ressed by a functional word", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "Suppose th,qt a constituent X is immediately followed by a constituent Y. We express a boundary-marker between X and Y by A-B, where A is a part-of-speech abbreviation of X's last word, and B is a 1)art-of-speech abbreviation of Y's first word. For instance, 'T' and \"go\" are a pronoun and a verb, respectively, so the marker \"pron-verb\" is inserted as abot, ndary marker into \"1 go\". Namely, \"I pron-verb go\", i.e. with the boundary marker inserted into the original input, matches tile pattern \"X pron-verb Y.\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Constituent I)()ulldary marke,\" exl)ressed by a functional word", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "Patterns are classified into (lffferent linguistic levels to limit the explosion of structural ambiguity during parsing. Table 2 shows typical linguistic levels in F.nglish patterns. In Table 2 , beginning phrase is the highest level, and compound word is the lowest. A variable on a given level is instantiated by a string described on that same level or on a lower level. For instance, in the noun phrase \"X of Y \", the variables, X and Y cannot be instantiated by a simple sentence.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 121, |
| "end": 128, |
| "text": "Table 2", |
| "ref_id": "TABREF1" |
| }, |
| { |
| "start": 186, |
| "end": 193, |
| "text": "Table 2", |
| "ref_id": "TABREF1" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Linguistic level", |
| "sec_num": "2.4" |
| }, |
| { |
| "text": "The algorithnl for constituertt l)oundary parsing is as follows;", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Derivation of Possible Structures", |
| "sec_num": "3" |
| }, |
| { |
| "text": "( (1) \"The bus leaves Kyoto at eleven a.m,\"", |
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| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Derivation of Possible Structures", |
| "sec_num": "3" |
| }, |
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| "text": "3.1 Assignment of nlorphohlgical int'ormathtn First, each word of the input string is assigned morphological information, such as its part-ol'-sl)eech and conjugation fc.rm. Through tiffs assignnient, we can get the lollowing part-of-speech sequence for (1).", |
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| "section": "Derivation of Possible Structures", |
| "sec_num": "3" |
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| "text": "(2) dot, noun, verb, propn, prop, num, suffix hi addition, each word is also assigned a thesaurus code for distance calcnhltions ,'lnd ,'ill index for retrieving l)atterns. For instance, \"bits\" has a thesaurus code corresponding to tile semantic attribute 'car.' Moreover, from the word \"(it\", we can obtain the index to the pattern \"X (at Y\", whicll is found for both verb phrase and nOl.ln phrase. A constituent boundary marker is inserted in an input string for pattern-matching. The marker is extracted [rein the part-of-speech sequence of an input sentence. Since such bigrams as dot-noun belong to the same constituent, marker insertion by a part-of-sl)eech bigram is restricted according to the items below. We mainttlin a list of p:lrt-of-speech bigrams that are eligible as marke,'s because they satisfy the above conditions. Of the bigrams in (2), \"det-noun\", \"propnprep\", \"prop-nora\", and \"nun>suffix\", vioklte the above conditions, and are of course excluded. Thus, only \"noun-verb\" and \"verb-propn\" are inserted into sentence (1), as shown in (3).", |
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| "section": "Derivation of Possible Structures", |
| "sec_num": "3" |
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| "text": "(3) \"The bus noun-verb leaves verb-propn Kyoto at eleven a.tn.\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Derivation of Possible Structures", |
| "sec_num": "3" |
| }, |
| { |
| "text": "Our pattern-nlatchhlg nlethod parses an inpilt sentence in a top-down fashion. The highest level patterns of the input sentence :.ire applied first; then lmtterns at lower levels are applied. The application procedure is as follows.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "al)l)liealhm of Ilaltel'ns", |
| "sec_num": "3.3" |
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| "text": "(I) Get indices to patterns from each woM of the sentence. With these indices, patterns are retrieved and chocked to determine if each of them can match tile sentence. Then exectlte (II).", |
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| "section": "al)l)liealhm of Ilaltel'ns", |
| "sec_num": "3.3" |
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| "text": "(ll)Try to apply the highest-level patterns first. If there is a pattern tlmt can be applied, execute (1II) with respect to the variable bindings. Otherwise, exectite (IV).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "al)l)liealhm of Ilaltel'ns", |
| "sec_num": "3.3" |
| }, |
| { |
| "text": "(Ill)Try to apply surface words (content words registered in a dictionary). If lhe al)lflicalion succeeds, the application fo, that portion is finished successfully. ()thcrwise, execute (I1).", |
| "cite_spans": [], |
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| "section": "al)l)liealhm of Ilaltel'ns", |
| "sec_num": "3.3" |
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| "text": "(IV) If the pattern to be applied is at the lowest level, the api)lication fails. Otherwise, lower tile level of the patterns and execute (II).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "al)l)liealhm of Ilaltel'ns", |
| "sec_num": "3.3" |
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| "text": "If pattern al~plication finishes successfully for all portions o[\" an input sentence, one or more source strttctures are obtained: since there is a possibility that more ttmn one pattern can be apl)lied to an expression in step (II), structural ambiguity may occur. We seek all possible structures by breadth-first application, and select the most plausible structure by the total distance value (See Section 4.4).", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "al)l)liealhm of Ilaltel'ns", |
| "sec_num": "3.3" |
| }, |
| { |
| "text": "In step (I), indices to possible patterns :-ire obtained from several words and bigrams in the marker-inserted sentence (3), as shown in Table 3 . (corot\u00d7rand word)", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 137, |
| "end": 144, |
| "text": "Table 3", |
| "ref_id": "TABREF2" |
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| "eq_spans": [], |
| "section": "al)l)liealhm of Ilaltel'ns", |
| "sec_num": "3.3" |
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| "text": "After step (I) is finished, steps (II)-(IV) are repeated recursively. First, the highest level pattern of the input sentence is applied. This is \"X noun-verb Y \", which is defined at the simple sentence level. Next, an attempt is made to apply patterns to the variable bindings \"the bus\" and \"leaves verb-propn Kyoto at eleven a.m.\", which are bound to variables X and Y, respectively. To \"the bus\", at compound word level p'tttern \"the X \" is applied first, and the surface word \"bus\" is applied to proso \"tile bus.\" Likewise, patterns and suri'aee words are appliecl Io tile remaining part, and tile al~plic:-nion is finished successfully.", |
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| "section": "al)l)liealhm of Ilaltel'ns", |
| "sec_num": "3.3" |
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| "text": "The pattern \"X at Y \" is found for both verb phrase and noun phrase. \"leaves verb-propn Kyoto at eleven a.m.\" thus has two possible structures, by the application of \"X at Y.\" \"X verb-propn Y \" at the verb phrase level and \"X a.m.\" at compotmd word level, are also applied. Fig. 1 is tile tree representation derived from the structure for sentence (1) where \"X at Y \" is a veal) phrase, while Fig. 2 is a tree representation derived from the slrnctllre in which \"X at Y \" is a noun phrase. A boldfilce denotes the head part in each pattent. This infer,nation is t, lilizcd for extracting an input for distance calculations (See section 4.3). Fig. 2 Struclure in which \"X at Y \" is a noun phrase tile thes:mrus, and varies from 0 to 1. Tim value 0 indicates that two semantic attributes belong to exactly the same category, and 1 indicates that they :-/re tmrclated. An expression consists of words. The distance between expressions is the sum of the (listance between words multiplied by each weight.", |
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| { |
| "start": 274, |
| "end": 280, |
| "text": "Fig. 1", |
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| { |
| "start": 394, |
| "end": 400, |
| "text": "Fig. 2", |
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| "start": 643, |
| "end": 649, |
| "text": "Fig. 2", |
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| "eq_spans": [], |
| "section": "al)l)liealhm of Ilaltel'ns", |
| "sec_num": "3.3" |
| }, |
| { |
| "text": "The distance is calculated quickly bectutse of the simple mechanism employed. (Sumita, 1992) and (Furuse, 1992 (Furuse, , 1994 give a clctailcd account of tile distance calculation mechanism we are aclopting.", |
| "cite_spans": [ |
| { |
| "start": 78, |
| "end": 92, |
| "text": "(Sumita, 1992)", |
| "ref_id": "BIBREF13" |
| }, |
| { |
| "start": 97, |
| "end": 110, |
| "text": "(Furuse, 1992", |
| "ref_id": "BIBREF0" |
| }, |
| { |
| "start": 111, |
| "end": 126, |
| "text": "(Furuse, , 1994", |
| "ref_id": "BIBREF12" |
| } |
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| "eq_spans": [], |
| "section": "al)l)liealhm of Ilaltel'ns", |
| "sec_num": "3.3" |
| }, |
| { |
| "text": "In this ,ruction, a nlethod for structural disaml)iguation utilizing dist,'mce calculation, is described.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Distance Calculatitm", |
| "sec_num": "4" |
| }, |
| { |
| "text": "The distance between two words is retluced to the distance between their respective sem;mtic attributes in a thesaurus. Words have associated thesaurus codes, which correspond to partietflar semantic attributes. The distance between the semantic attributes is determined according to the relationship of their positions in the hierarchy of", |
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| "section": "Distance", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "The advantages of an example-based framework are mainly due to the distance calctdation, which achieves the bcst-malch operation between tile input and provided examples.", |
| "cite_spans": [], |
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| "section": "Best-match by distance calcul:ltinn", |
| "sec_num": "4.2" |
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| { |
| "text": "In TDMT, translation is performed by applying stored empirical Iransl'er knowledge. In TDMT transfer knowledge, each source pattern has example words of variables and possible target patterns. The most \u2022 qppropriate target pattern is selected according to the calculated distance between, the input words and the example words. The English pattern \"X at Y \" at the verb phrase level, corresponds to several possible Japanese expressions, as shown in the folhlwing English-to-Japanese transfer knowledge: ((present, conference) ..), Y ' ni X' ((stay, hotel) ..), ((look, it) ..)", |
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| "start": 504, |
| "end": 526, |
| "text": "((present, conference)", |
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| { |
| "start": 534, |
| "end": 556, |
| "text": "' ni X' ((stay, hotel)", |
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| "start": 562, |
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| "text": "((look, it)", |
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| } |
| ], |
| "eq_spans": [], |
| "section": "Best-match by distance calcul:ltinn", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "XatY => Y' de X'", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Best-match by distance calcul:ltinn", |
| "sec_num": "4.2" |
| }, |
| { |
| "text": "The first possible target pattern is \" Y' de X' \", with example set ((present, cotg'erenee) ..). We will see that this target pattern is likely to be selected to the extent that the input variable bindings are semanticqlly similar to the example elements \"present\" and \"coati're|Ice.\"", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 68, |
| "end": 91, |
| "text": "((present, cotg'erenee)", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Y' we X'", |
| "sec_num": null |
| }, |
| { |
| "text": "Within this pattern, X' is the target word correslx)nding to X, tile result of transfer. \"preset, l\" and \"con/~reaee\" are sample bindings for \" X at Y \", where X = \"present\", and Y = \"conference\". The al)ove transfer knowledge is compiled from such translation examples as the source-target pair of \" presem a paper at the conference\" and \"kaigi de ronbun wo happ),ou-st~ru\", where \"kaigi\" means \"conference\" and \"happyou-sltru\" means \"present\".", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Y' we X'", |
| "sec_num": null |
| }, |
| { |
| "text": "Tilt semantic distance from the input is calculated for all examples. Then lhe example with the least distance from the input is chosen, and the target expresskm of that example is extracted. If the input is closest to (stay, hotel), \"Y' ni X' \" is chosen as the target express ion.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Y' we X'", |
| "sec_num": null |
| }, |
| { |
| "text": "The enrichment of examples increases tile aCc,lracy Of determining the target expression and structure because conditions become more dclailed.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Y' we X'", |
| "sec_num": null |
| }, |
| { |
| "text": "An input for distance ealcuh.ltion consists of head words in variable parts. In \"X at Y \" for the structure in Fig. l, X and Y are substitumd [or the compound expressions, \"leaves verb-propn Kyoto\" a1~d \"eleven a.m.\", respectively. In such eases, it is necessary to extract head words as the input for the disEmce calculation about \"X at Y \".", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 111, |
| "end": 120, |
| "text": "Fig. l, X", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "lnl)ut of' distance calculation", |
| "sec_num": "4.3" |
| }, |
| { |
| "text": "In order to get head words, tile head part is (lcsignawd in each pattern (boldface in Figs. 1 and 2 ). For inslance, the t)attern \"X vorb-propn Y II e(li)t;lillg the information that X is a head part. So the head of \"leaves verb-propn Kyoto\" is \"leaves\", and tile head or \"x a.m.\" is \"a.m.\". Thus, in \"X at Y \" for Ihe strncture in Fig. 1 , the ini)ut of the distance calculation is (leaves, a.m.) . Table 4 shows tile result of distance cqlculation in \"X at Y \" in Fig. 1 . The most plausible target structure \"Y' ni X' \" and its distance value 0.17 are obtained by the dislance calculation.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 86, |
| "end": 99, |
| "text": "Figs. 1 and 2", |
| "ref_id": null |
| }, |
| { |
| "start": 332, |
| "end": 338, |
| "text": "Fig. 1", |
| "ref_id": null |
| }, |
| { |
| "start": 383, |
| "end": 397, |
| "text": "(leaves, a.m.)", |
| "ref_id": null |
| }, |
| { |
| "start": 400, |
| "end": 407, |
| "text": "Table 4", |
| "ref_id": null |
| }, |
| { |
| "start": 466, |
| "end": 472, |
| "text": "Fig. 1", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "lnl)ut of' distance calculation", |
| "sec_num": "4.3" |
| }, |
| { |
| "text": "Head words are passed upward from lower palterns to higher 1)atterns. Since the head of the verb phrase pattern, \"X at Y \" is assigned te X, the head of \"leaves verb-propn Kyoto at eleven a.m.\" is \"leaves\", which is tile head of \"leaves wrb-propn Kyoto\". The head of \"the bus\" is \"bus\" fi'om the head information that the Table 4 Result of distance calculation in \"X a/Y \" in lqg. 1 input: (leave, a.m.) AL~J\u00a3ELeXxl)ression closest example and |IS value :~ Y' de X' (arrive, a.m.) O. 17 Y' ni X' (serve, reception) 0.67 Y' we X' (look, it) 1.00 head of \"the X \" is X. Thus, rite input of tile distance calculation of \"X noun-verb Y \" is (bits, leave).", |
| "cite_spans": [ |
| { |
| "start": 390, |
| "end": 403, |
| "text": "(leave, a.m.)", |
| "ref_id": null |
| }, |
| { |
| "start": 460, |
| "end": 514, |
| "text": "de X' (arrive, a.m.) O. 17 Y' ni X' (serve, reception)", |
| "ref_id": null |
| }, |
| { |
| "start": 529, |
| "end": 539, |
| "text": "(look, it)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 322, |
| "end": 329, |
| "text": "Table 4", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "lnl)ut of' distance calculation", |
| "sec_num": "4.3" |
| }, |
| { |
| "text": "Distance calculqtion selects not only the most l)lausible target expression but also the most plausible source structure. When .strtlcttlral aml)iguity occttrs, the most apllrOl)riate structure is selected by comt)uting tl~o totals for all possible combinations of partizfl distance values. The structure with the least total distance is judged most consistent wilh empirical knowledge, and is chosen as Ihe most 1)lausil)le structure (Furuse 1992 (Furuse , 1994 Sumita 1993) . Table 5 shows the result of each partial distance talc|Ha|ion for tile structure in Fig. 1 . l:mm Table 5 , we V.Ct Ihe total distance value 1.17 for the structure in l:it;. 1.", |
| "cite_spans": [ |
| { |
| "start": 435, |
| "end": 447, |
| "text": "(Furuse 1992", |
| "ref_id": "BIBREF0" |
| }, |
| { |
| "start": 448, |
| "end": 462, |
| "text": "(Furuse , 1994", |
| "ref_id": "BIBREF12" |
| }, |
| { |
| "start": 463, |
| "end": 475, |
| "text": "Sumita 1993)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 478, |
| "end": 485, |
| "text": "Table 5", |
| "ref_id": null |
| }, |
| { |
| "start": 562, |
| "end": 568, |
| "text": "Fig. 1", |
| "ref_id": null |
| }, |
| { |
| "start": 576, |
| "end": 583, |
| "text": "Table 5", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "SI,'uetural dis:mlbignation", |
| "sec_num": "4.4" |
| }, |
| { |
| "text": "Result of each partial distance calculation for tile slructure in I,'ig. 1 souiee chosen l~lr..~c:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Table 5", |
| "sec_num": null |
| }, |
| { |
| "text": "[ distance val,lg the X X' 0.33 X rlotJrl-vorb Y X' wa Y' 0.67 X verb-propn Y Y' we X' 0.00 X .t Y Y\" ni X' 0.17 X a.m. gozeJ~ X'ji 0.00", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Table 5", |
| "sec_num": null |
| }, |
| { |
| "text": "The difference in total distance value I)etween two l)OSsible structures for sentence (1) is due only to the distance value of \"X at Y \", for the structure in Figs. 1 and 2 . For the strucltne in Fig. 2 , the distance valtl0 of \"X at Y \" at tile neun phrase level is given as 0.83, as shown in Table 6 , and is given a total distance ef 1.83. Thus, the structure in Fig. 1 is selected as the 3 The:.;e vii]ties were col//pu,ed based on Ihe present transfer knowledge of the T1)MT system. appropriate restflt because it has the least total distance knowledge for the pattern \"X pron-noun Y \"; value. Table 6 Result of distance calcul,ltion in \"X at Y \" in Fig. 2 input:(Kyoto, a.m.) target expression \u00a2losest exampl0 and its value Y' no X' (room, hotel) 0.83 Y' deno X' (language, conference) 1.00", |
| "cite_spans": [ |
| { |
| "start": 741, |
| "end": 754, |
| "text": "(room, hotel)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [ |
| { |
| "start": 159, |
| "end": 173, |
| "text": "Figs. 1 and 2", |
| "ref_id": null |
| }, |
| { |
| "start": 197, |
| "end": 203, |
| "text": "Fig. 2", |
| "ref_id": null |
| }, |
| { |
| "start": 295, |
| "end": 302, |
| "text": "Table 6", |
| "ref_id": null |
| }, |
| { |
| "start": 367, |
| "end": 373, |
| "text": "Fig. 1", |
| "ref_id": null |
| }, |
| { |
| "start": 600, |
| "end": 607, |
| "text": "Table 6", |
| "ref_id": null |
| }, |
| { |
| "start": 656, |
| "end": 683, |
| "text": "Fig. 2 input:(Kyoto, a.m.)", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Table 5", |
| "sec_num": null |
| }, |
| { |
| "text": "In macbine translation, it ix important to disambiguate tbe possible structures, l)ecause a difference in structure may bring about a translation difference. For instance, the structures in Figs.1 and 2 give different Japanese translations (4) and (5), respectively. (4) is selected because it is generated from the best structure with the least total distance value.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 190, |
| "end": 202, |
| "text": "Figs.1 and 2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Table 5", |
| "sec_num": null |
| }, |
| { |
| "text": "(4) basu wa gozen 11 ji ni Kyoto we de masu 4", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Table 5", |
| "sec_num": null |
| }, |
| { |
| "text": "(5) basu wa gozen ] 1 ji ~_ Kyoto we de masu", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Table 5", |
| "sec_num": null |
| }, |
| { |
| "text": "Since a postposition is quite often used as a caseparticle in Japanese, tim botmdary markers expressed by a part-of-speech bigram may not be used less frequently than in English. However, in spoken Japanese, postpositions are frequently omitted. The Jqpanese sentence \"Kochira wa jimukyoku\" where kochira means this and jimukyoku means \"office\", is translated into the English sentence \"77fis is the office\" by applying transfer knowledge such as the following5:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Constituent Boundary Parsing in Japanese", |
| "sec_num": "5" |
| }, |
| { |
| "text": "XwaY => X'be Y' But postpositions are often omitted in natural six)ken Japanese, e.g. in the sentence \"Kochira jimukyoku.\" The sentence can thus be divided into two noun phrases, \"kochira\" and \"jimukyoku.\" \"kochira\" is a pronotm, and \"jimukyoku\" is a noun. So, using the bigram method of marking boundaries, we get \"Kochira pronnoun jimukyoku\", where the bigram \"pron-noun\" was inserted. The English sentence \"77fis is the oJfice\" can then be produced by applying the following transfer 4\"basu\", \"de\", and \"masu\" mean \"bus\", \"leave\", and a polite sentence-final form, respectively. 5 For simplicity, examples and other possible target expressions are omined.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Constituent Boundary Parsing in Japanese", |
| "sec_num": "5" |
| }, |
| { |
| "text": "In Japanese adnominal expressions, too, constituei~t bonndary markers ,'Ire inserted between the modifier and the modified.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "X pron-noun Y => X' be Y'", |
| "sec_num": null |
| }, |
| { |
| "text": "We have evaluated tim efficiency of our parsing method by utilizing a Japanese-lo-English (Jg) and English-to-Japanese (E J) TDMT prototype system (Furuse 1994; Sobashima 1994) , which ix ,'unning on a Symbolics XL120(I, a LISP machine with IOMIPS performance. The system's domain is inquiries concerning international conference registrations. The efficency is evaluated with 154 Japanese sentences and 138 corresl)onding English sentences, which are extracted from 10 dialogues in the domain. The systeln has al)out 500 source p,'llterns for JE translation and about 35(1 source patterns for EJ transhttion.", |
| "cite_spans": [ |
| { |
| "start": 147, |
| "end": 160, |
| "text": "(Furuse 1994;", |
| "ref_id": "BIBREF12" |
| }, |
| { |
| "start": 161, |
| "end": 176, |
| "text": "Sobashima 1994)", |
| "ref_id": "BIBREF12" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Results", |
| "sec_num": "6" |
| }, |
| { |
| "text": "The test sentences mentioned above have already l)een tr:tined to investigate the efficiency of the method, and can be p-lrse(l correctly by the system. Table 7 outlines the 154 Japanese sentences and 138 corresponding English sentences. contrast, tile Jai)aneso l)ostposition does not generally produce different-level constituents. Table 8 shows how ,nuch time it takes to reach the best structure and translation output in our JE and EJ TDMT system. The processing time for distance calculation includes strnctnral disaml)iguation in addition to ktrget pattern selection.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 153, |
| "end": 160, |
| "text": "Table 7", |
| "ref_id": "TABREF4" |
| }, |
| { |
| "start": 334, |
| "end": 341, |
| "text": "Table 8", |
| "ref_id": "TABREF5" |
| } |
| ], |
| "eq_spans": [], |
| "section": "Results", |
| "sec_num": "6" |
| }, |
| { |
| "text": "Tiffs demonstrates that the ot~r parsing method can get the best structure and translation output quickly wit]fin the examl)lo-/xlsed framework. A constituent boundary parsing method for cxaniplobased in;ichinB translation has been propose{I, l,inguislio patterns consisthlg of variables and constituent boundaries, are applied to an input string in a top-down fashion, and the possible structures can bc {lisambigutated using distance calculation by the examl}le-based framework. This nlothod is cll'icicut, and useful for parsing bolh Japanese and Knglish sentences. TIle \"['DMT system, which bidirectionally translates between Jal/anese and English within the eXaml)le-b:~sed framework, utilizes this parsing method and achieves efficient and robust spokel) larlguage translation.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Results", |
| "sec_num": "6" |
| }, |
| { |
| "text": "By introducing linguistic information to more patterns, there is a possibility that this method can also be utilized for ruled}ased MT, deep soinantic analysis, and so on. We will improve our parser by increasing the number of lraining sentences, and test its accuracy on olvn dala.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Results", |
| "sec_num": "6" |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "The authors wotlld like to th-lnk the menlbors of ATP, Interpreting Telecomnlllilicatioiis P, esoarch Laboratories for their colnlrlOnls oi1 variotls p,'irts of lhi~, research. Special thanks are due 1o Kohei [labara and g:lsuhiro Yamazaki, for their snl)l)ort of this research.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Acknowledgements", |
| "sec_num": null |
| } |
| ], |
| "bib_entries": { |
| "BIBREF0": { |
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| "ref_entries": { |
| "FIGREF0": { |
| "text": "A) Assignment of morphological inRn'nmtion to each woM of an input string (B) Insertion of constituent boundary nmrkcrs (C) Derivation of possible structures by top-down pattern matching (D) Structural disambiguation by distance calculation Note: we will explain (A), (B) and (C) in this section, and (D) in the next section, usirlg die following English sentence;", |
| "num": null, |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "FIGREF2": { |
| "text": "(a) Neither A nor B is a part-of-speech relating two constituents, such as a preposition (b) A is not a l)art-of-speech nlodifying a latter constituent, such :.is a dotorinh/or.(c) B is not a l)art-of-sI)eech modifying a previous constituent, such as a suffix.", |
| "num": null, |
| "uris": null, |
| "type_str": "figure" |
| }, |
| "TABREF0": { |
| "type_str": "table", |
| "content": "<table><tr><td/><td colspan=\"2\">English parts-of-speech</td></tr><tr><td>~of-speech</td><td>abbreviation</td><td>example</td></tr><tr><td>adjective</td><td>adj</td><td>large</td></tr><tr><td>adverb</td><td>adv</td><td>exactly</td></tr><tr><td>interjection</td><td>i nterj</td><td>oh</td></tr><tr><td>common noun</td><td>noun</td><td>bus</td></tr><tr><td>numeral</td><td>num</td><td>eleven</td></tr><tr><td>proper noun</td><td>propn</td><td>Kyoto</td></tr><tr><td>pronotm</td><td>pron</td><td>I</td></tr><tr><td>wh-word</td><td>wh</td><td>what</td></tr><tr><td>verb</td><td>verb</td><td>go</td></tr><tr><td>be-verb</td><td>be</td><td>is</td></tr><tr><td>auxiliary verb</td><td>aux</td><td>crm</td></tr><tr><td>preposition</td><td>prep</td><td>ca</td></tr><tr><td>conjunction</td><td>co nj</td><td>bta</td></tr><tr><td>determiner</td><td>det</td><td>the</td></tr><tr><td>suffix</td><td>suffix</td><td>a.m.</td></tr></table>", |
| "text": "", |
| "num": null, |
| "html": null |
| }, |
| "TABREF1": { |
| "type_str": "table", |
| "content": "<table><tr><td>level</td><td>exan_!p_le</td></tr><tr><td>beginning phrase</td><td>excuse me but X</td></tr><tr><td>conlpotlnd sentence</td><td>X when V</td></tr><tr><td>simple sentence</td><td>I would like to X</td></tr><tr><td>verl) phrase</td><td>X at Y</td></tr><tr><td>noun phrase</td><td>XofY, XatY</td></tr><tr><td>c()mpound word</td><td>X o'clock</td></tr></table>", |
| "text": "Typical levels in English patterns", |
| "num": null, |
| "html": null |
| }, |
| "TABREF2": { |
| "type_str": "table", |
| "content": "<table><tr><td/><td colspan=\"2\">ReUieved patterns from (3)</td></tr><tr><td>word</td><td colspan=\"2\">retrieved pattern (lilmuistic level)_</td></tr><tr><td>the</td><td>tt, e X</td><td>(compound word)</td></tr><tr><td>noun-verb</td><td colspan=\"2\">X noun-verb Y (simple sentence)</td></tr><tr><td>verb-propn</td><td colspan=\"2\">X verb-propn Y (verb pltrasc)</td></tr><tr><td>at</td><td>X at Y</td><td>(verb phr:~se, noun phrase)</td></tr><tr><td>a. ?l'l.</td><td>X a.m.</td><td/></tr></table>", |
| "text": "", |
| "num": null, |
| "html": null |
| }, |
| "TABREF4": { |
| "type_str": "table", |
| "content": "<table><tr><td colspan=\"2\">Outline of test senlences</td><td/></tr><tr><td>_</td><td colspan=\"2\">Japanese E_j1Aj_I i sh</td></tr><tr><td>words per inpnt sentence</td><td>9.8</td><td>8.7</td></tr><tr><td colspan=\"2\">average numl)er of ix)ssible structures 1.5</td><td>4.8</td></tr></table>", |
| "text": "An l-nglish sentence tends to have more struclural ambiguities than a Japanese sentence, bec,'tnse of PF'altachment, the phenomenon that an English prepositionf)rodtlCCS [)()[h a noun verb p]lrasc [Ilia a [iolln phasc. In", |
| "num": null, |
| "html": null |
| }, |
| "TABREF5": { |
| "type_str": "table", |
| "content": "<table><tr><td/><td>JF.</td><td>E,I</td><td>6</td></tr><tr><td colspan=\"3\">derivation of possible structures 0.25 (scc) 0.l 7</td></tr><tr><td>dislance calculation</td><td>1.32</td><td>0.14</td></tr><tr><td>whole tr,'lnsl;ition</td><td>2.17</td><td>1.07</td></tr><tr><td>7 Conclu{lhlg Renllirks</td><td/><td/></tr></table>", |
| "text": "Processing time for the TI)MT system", |
| "num": null, |
| "html": null |
| } |
| } |
| } |
| } |
