| { |
| "paper_id": "A83-1027", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T02:11:29.078827Z" |
| }, |
| "title": "OF MONTAGUE GRAMMAR TO ENGLISH-JAPANESE MACHINE TRANSLATION", |
| "authors": [ |
| { |
| "first": "Toyoaki", |
| "middle": [], |
| "last": "Nishida", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Kyoto University", |
| "location": { |
| "addrLine": "Sakyo-ku", |
| "postCode": "606", |
| "settlement": "Kyoto", |
| "country": "JAPAN" |
| } |
| }, |
| "email": "" |
| }, |
| { |
| "first": "Shuji", |
| "middle": [], |
| "last": "Doshita", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "Kyoto University", |
| "location": { |
| "addrLine": "Sakyo-ku", |
| "postCode": "606", |
| "settlement": "Kyoto", |
| "country": "JAPAN" |
| } |
| }, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "English-Japanese machine translation requires a large amount of structural transformations in both grammatical and conceptual level. In order to make its control structure clearer and more understandable, this paper proposes a model based on Montague Gramamr. Translation process is modeled as a data flow computation process. Formal description tools are developed and a prototype system is constructed. Various problems which arise in this modeling and their solutions are described. Results of experiments are shown and it is discussed how far initial goals are achieved. I. GOAL OF INTERMEDIATE REPRESENTATION DESIGN Differences between English and Japanese exist not only in grammatical level but also in conceptual level. Examples are illustrated in", |
| "pdf_parse": { |
| "paper_id": "A83-1027", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "English-Japanese machine translation requires a large amount of structural transformations in both grammatical and conceptual level. In order to make its control structure clearer and more understandable, this paper proposes a model based on Montague Gramamr. Translation process is modeled as a data flow computation process. Formal description tools are developed and a prototype system is constructed. Various problems which arise in this modeling and their solutions are described. Results of experiments are shown and it is discussed how far initial goals are achieved. I. GOAL OF INTERMEDIATE REPRESENTATION DESIGN Differences between English and Japanese exist not only in grammatical level but also in conceptual level. Examples are illustrated in", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": ".", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "Accordingly, a large amount of transformations in various levels are required in order to obtain high quality translation. The goal of this research is to provide a good framework for carrying out those operations systematically. The solution depends on the design of intermediate representation (IR). Basic requirements to intermediate representation design are listed below. a) Accuracy: IR should retain logical conclusion of natural language expression.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "The following distinctions, for example, should be made in IR level: it is often the case that a given English word must be translated into different Japanese words or phrases if it has more than one word meanings.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "But it is not reasonable to capture this problem solely as a problem of word meaning disambiguation in analysis phase; the needed depth of disamb\u00a3iuation depends on target language.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "So it is also handled in transfer phase.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "In general, meaning of \u2022 given word is recognized based on the relation to other constituents in the sentence or text vhicb is semantically related to the given word. To make this poaslble in transfer phase, IR must provide a link to semantically related constituents of a given item.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "For example, an object of a verb should be accessible in IR level from the verb, even if the relation is implicit ~n the surface structure (as., passives, relative claus=a, and their combinations, etc.) \u00a2) Prediction of control:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "given an IR expression, the model should be able to predict explicitIy what operations are co be done in what order.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "", |
| "sec_num": null |
| }, |
| { |
| "text": "some sort of transformation rules ere word specific.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "d) Lexicon driven:", |
| "sec_num": null |
| }, |
| { |
| "text": "The IR interpretation system should be designed Co deal with those word specific rules easily. e) Computability:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "d) Lexicon driven:", |
| "sec_num": null |
| }, |
| { |
| "text": "All processing= should be effectively computable.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "d) Lexicon driven:", |
| "sec_num": null |
| }, |
| { |
| "text": "Any IR is useless if it is not computable.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "d) Lexicon driven:", |
| "sec_num": null |
| }, |
| { |
| "text": "This section outlines our solution Co the requirements posed in the preceding section.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "PRINCIPLE OF TP, ANSLATION", |
| "sec_num": "2." |
| }, |
| { |
| "text": "We employ MonCague Gram=mr (HonCague 1974, Dowry 1981) as a theoretical basis of translation model. Inter~edlate representation is designed based on intensional logic.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "PRINCIPLE OF TP, ANSLATION", |
| "sec_num": "2." |
| }, |
| { |
| "text": "Intermediate representation for a given natural language expression is obtained by what we call functional analysis.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "PRINCIPLE OF TP, ANSLATION", |
| "sec_num": "2." |
| }, |
| { |
| "text": "In functional analysis, input sentence is decomposed into groups of constituents and interrelationship among those groups are analyzed in terms of function-argument relationships. Suppose a sentence:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "I don't have a book.", |
| "eq_num": "(l)" |
| } |
| ], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "The functional analysis makes following two points:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "a) (L) is decomposed as:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "\"I have a book\" \u00f7 \"nOt\".", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "(2) b) In the decomposition (2), \"not\" is an operator or function co \"I have a book.\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "The result of this analysis can be depicted as follows:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "~ \"\"I have a book\" I (3)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "wherel >denotes a function and[ Idenotes en argument. The role of \"not\" as a function is:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "\"not\" as a semantic operstor: it negates a given proposition; \"not\" is a syntactic operator:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "it inserts an appropriate auxiliary verb and = lexical item \"not\" into appropriate position of its argument.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "This kind of analysis goes on further with embedded sentence until it is decomposed into lexical units or even morphemes.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Functional Analysis", |
| "sec_num": "2.1" |
| }, |
| { |
| "text": "Montague Grammar (MG) gives a basis of functlonel analysis.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Montague Grammar as a Basic Theory", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "One of the advantages of MG consists in its interpretation system of function form (or intensional logical form).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Montague Grammar as a Basic Theory", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "In MG, interpretation of an intenelonal logical formula is a mapping I from incenaional logical formulas to set theoretical domain.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Montague Grammar as a Basic Theory", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "Important property is chat this ampping I is defined under the cons-trainC of compositlonality, that is, I satisfies: A For the sake of property (5), ~he interpretation of (6) is done as a data flow computation process as followa:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Montague Grammar as a Basic Theory", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "Z[f(a,b .... )]'I[fl(Ha],Z[b] .... ),", |
| "eq_num": "(5)" |
| } |
| ], |
| "section": "Montague Grammar as a Basic Theory", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "A ~I[A] , | A \"I Its c O } ~7)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Montague Grammar as a Basic Theory", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "By this property, we can easily grasp the processing stream.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Montague Grammar as a Basic Theory", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "In particular, we can easily ~hooc trouble and source of abnormality when debugging a system. Due to the above property and others, Ln particular due to its rigorous framework based .)n Logic, MG has been studied in ~nformation science field (Hobbs 1978 , Friedman |978, Yonezaki [980, Nishida 1980 , Landsbergen 1980 , Moran 1982 , Moore 1981 , Rosenschein 1982 .", |
| "cite_spans": [ |
| { |
| "start": 242, |
| "end": 253, |
| "text": "(Hobbs 1978", |
| "ref_id": "BIBREF5" |
| }, |
| { |
| "start": 254, |
| "end": 298, |
| "text": ", Friedman |978, Yonezaki [980, Nishida 1980", |
| "ref_id": null |
| }, |
| { |
| "start": 299, |
| "end": 317, |
| "text": ", Landsbergen 1980", |
| "ref_id": "BIBREF6" |
| }, |
| { |
| "start": 318, |
| "end": 330, |
| "text": ", Moran 1982", |
| "ref_id": "BIBREF10" |
| }, |
| { |
| "start": 331, |
| "end": 343, |
| "text": ", Moore 1981", |
| "ref_id": "BIBREF9" |
| }, |
| { |
| "start": 344, |
| "end": 362, |
| "text": ", Rosenschein 1982", |
| "ref_id": "BIBREF12" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Montague Grammar as a Basic Theory", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "Application of MG to machine translation was also attempted (Hauenschild 1979 , Landsbergen 1982 , but those systems have only partially utilized the power of MG. Our approach attempts to utilize the full power of MGo", |
| "cite_spans": [ |
| { |
| "start": 60, |
| "end": 77, |
| "text": "(Hauenschild 1979", |
| "ref_id": "BIBREF3" |
| }, |
| { |
| "start": 78, |
| "end": 96, |
| "text": ", Landsbergen 1982", |
| "ref_id": "BIBREF7" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Montague Grammar as a Basic Theory", |
| "sec_num": "2.2" |
| }, |
| { |
| "text": "In order to obtain the syntactic structure in Japanese from an intensional logical form, in the same way as interpretation process of MC, we change the semantic domain from set theoretical domain to conceptual domain for Japanese.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "Each conceptual unit contains its syntactic expression in Japanese.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "Syntactic aspect is stressed for generating syntactic structure in Japanese.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "Conceptual information is utilized for semantic based word choice end paraphrasing.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "For example, the following function in Japanese syntactic domain is assigned to \u2022 logical item \"not\":", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "(LAMBDA (x) (SENTENCE x [AUX \"NAI\"])).", |
| "eq_num": "(8)" |
| } |
| ], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "3.1 Definition of Formal Tools e) English oriented Formal Representation (EFR) is a version of intensional logic, and gives a rigorous formalism for describing the results of functional analysis.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "It is based on Cresswell's lambda deep structure (Cresawell 1973) . Each expression has a uniquely defined type. Lambda form is employed to denote function itself. b) Conceptual Phrase Structure (CPS) is a data structure in which syntactic and semantic information of a Japanese lexicel unit or phrase structure are packed.", |
| "cite_spans": [ |
| { |
| "start": 49, |
| "end": 65, |
| "text": "(Cresawell 1973)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "i) example of CPS for a lexical item:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "EIGO:[NP \"EIGO\" with,ZSAmLANGUAGE; ...,]", |
| "eq_num": "(9)" |
| } |
| ], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "category; lexical item; conceptual info.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "; \"EIGO\" means English\" language.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "ii) example of CPS for phrase structure:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "[NP [ADJ \"AKAI\" with ... ] [NOUN \"RINGO\" with ... ] with ... ] (i0)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "Transfer-generation process for the sentence (1) looks like:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "\"I don't have a book\" ~',,I have a book\" I // \u2022 TRANSFER / (LAMBDA (x) {SENTENCE x [AUX \"NAI\"]}) TRANS FE R, GENE RAT I ON S WATASHI-WA HON-WO MOTSU ,,-..._./ S S AUX", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Application of Montague Grammar to Machine Translation", |
| "sec_num": "2.3" |
| }, |
| { |
| "text": "; \"AKAI\" means red, and \"RINGO\" means apple. c) CPS Form (CPSF) is a form which denotes operation or function on CPS domain.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "WATASHI-WA HON-WO MOTSU NAI MOTANAI", |
| "sec_num": null |
| }, |
| { |
| "text": "It is used to give descriptions to mappings from EFR to CPS.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "WATASHI-WA HON-WO MOTSU NAI MOTANAI", |
| "sec_num": null |
| }, |
| { |
| "text": "i) Constants: CPS. ii) Variables:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Constituents of CPSF are:", |
| "sec_num": null |
| }, |
| { |
| "text": "x, y, ... . (indicated by lower case strings).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Constituents of CPSF are:", |
| "sec_num": null |
| }, |
| { |
| "text": "iii) Variables with constraints: e.g., (! SENTENCE x).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Constituents of CPSF are:", |
| "sec_num": null |
| }, |
| { |
| "text": "; variable x which must be of category SENTENCE. ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Constituents of CPSF are:", |
| "sec_num": null |
| }, |
| { |
| "text": ".. CPSF ..", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "~,RAN$FER)", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 . CPS ..", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "~,RAN$FER)", |
| "sec_num": null |
| }, |
| { |
| "text": "Prefix notation is used for CPSF, described using Formal Tools. / and syntactic aspect is emphasized.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.2. Example of Translation Process //", |
| "sec_num": null |
| }, |
| { |
| "text": "stage 3 (generation): evaluates the CPSF to get CPS; generation of surface structure from CPS is straightforward.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.2. Example of Translation Process //", |
| "sec_num": null |
| }, |
| { |
| "text": "In order to give readers an overall perspective, we illustrate an example in Fig.2 . Note that the example illustrated includes partial negation.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 77, |
| "end": 82, |
| "text": "Fig.2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "Fig.2. Example of Translation Process //", |
| "sec_num": null |
| }, |
| { |
| "text": "Thus operator \"not\" is given a wider scope than \"always\".", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.2. Example of Translation Process //", |
| "sec_num": null |
| }, |
| { |
| "text": "In the remaining part of this section we will describe how to extract EFR expression from a given sentence. Then we will discuss the problem which arises in evaluating CPSF, and give its possible solution.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.2. Example of Translation Process //", |
| "sec_num": null |
| }, |
| { |
| "text": "Rules for translating English into EFR form in .~ssociated with each phrase structure rules.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Extracting EFR Expression from Input Sentence", |
| "sec_num": "3.2" |
| }, |
| { |
| "text": "For example, the rule looks llke:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "159", |
| "sec_num": null |
| }, |
| { |
| "text": "NP -> DET+NOUN where <NP>-<DET>(<NOUN>) (ii)", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "159", |
| "sec_num": null |
| }, |
| { |
| "text": "where, <NP> stands for an EFR form assigned tu ~he NP node, etc. Rule (II) says chat EFR for an NP is a form whose function section is EFR for a DET node and whose argument section is EFR for a NOUN node. This rule can be incorporated into conventional natural language parser.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "159", |
| "sec_num": null |
| }, |
| { |
| "text": "Evaluation process of CPSF is a sequence of lambda conversions and tree ~ransformations. Evaluation of CPSF is done by a LISP ~ncerpreter-l i ke al gori t hm. A pr obl em whi ch we cal l hi gher order problem arose in designing the evaluation algorithm.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Evaluation of CPSF", |
| "sec_num": "3.3" |
| }, |
| { |
| "text": "By higher order property we mean that there exist functions which take other functions as arguments (Henderson 1980) . CPSF in fact has this property.", |
| "cite_spans": [ |
| { |
| "start": 100, |
| "end": 116, |
| "text": "(Henderson 1980)", |
| "ref_id": "BIBREF4" |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "For example, an adjective \"large\" is modeled as a function which takes a noun as its argument.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "For example, large(database), \"large database\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "On the other hand, adverbs are modeled as functions to adjectives, For example, very(large), extremely(large), comparatively(large), etc.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "The difficulty with higher order functions consists in modifiction to function. For explanation, let our temporal goal be regeneration of English from EFR.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "Suppose we assign to \"large\" a lambde form like:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "(LAMBDA (x) (NOUN [ADJ \"LARGE\"] x>)", |
| "eq_num": "(14)" |
| } |
| ], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "which takes a noun and returns a complex noun by attaching an adjective \"large\". If the adjective is modified by an adverb, say \"very\", we have to modify (14); we have to transform (14) into a lambda form like:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "(LASBDA (x) (NOUN [ADJ [ADV \"VERY\"] [ADJ \"LARGE\"]] x}),", |
| "eq_num": "(15)" |
| } |
| ], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "which attaches a complex adjective \"very large\" to a given noun. As is easily expected, it is too tedious or even impossible to do this task in general. Accordingly, we take an alternative assignment instead of (14), namely:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "large <-[ADJ \"LARGE\"].", |
| "eq_num": "(16)" |
| } |
| ], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "Since this decision cuases a form:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "[ADJ \"LARGE\"]([NOUN \"DATABASE\"]),", |
| "eq_num": "(17)" |
| } |
| ], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "to be created in the course of evaluation, we specify what to do in such case. The rule is defiend as follows: ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "[ADj]([NOUN]) -[NOUN [ADJI [NOUN]].", |
| "eq_num": "(18)" |
| } |
| ], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "(Ax[(((*ap(on))(y))(block))(x)])],", |
| "eq_num": "(20)" |
| } |
| ], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "; which may read: is y:[there is a uniquely specified object y referred to by an NP \"the table\", such that y is a block which is restricted to be located on x.]", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "This lambda form is too complicated for tree transformation procedure to manipulate.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "So it should be transformed into equivalent CPS if it exists.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "The type of the lambda form is known from the context, namely one-place predicate. So if we apply the lambda form (20) to \"known\" entity, say \"it\", we can obtain sentence structure like: ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "SENTENCE UN PRED NOUN l NP NP JOSHI I /',,", |
| "eq_num": "/'" |
| } |
| ], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "The extraction rule can be written as a pattern matching rule like:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "SENTENCE NP NP PRED I \\ SORE WA x:NOUN DEARU (It is ~ z) x", |
| "eq_num": "(23)" |
| } |
| ], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "This rule is called an application rule.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "In general, evaluation of [ambda form itself results in a function value (function as a value).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "This causes difficulty as mentioned above. Unfortunately, we can't dispense with lambda forms; lambda variables are needed to link gap and its antecedent in relative clause, verb and its dependants (subject, object, etc), preposition and its object, etc. For example, in our model, an complex noun modified by a PP: \"block on the table\"", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "\u00a3s assigned a following EFR:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "Of course, this way of processing is not desirable; it introduces extra complexity. But this is a trade off of employing formal semantics; the same sort of processing is also done rather opaque procedures in conventional MT system.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Higher Order Problem", |
| "sec_num": null |
| }, |
| { |
| "text": "This section illustrates how English-Japanese translation process is modeled using formal tools.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "MODELING TRANSLATION PROCESS", |
| "sec_num": "4." |
| }, |
| { |
| "text": "Firstly, how several basic linguistic constructions are treated is described and then mechanism for word choice is presented.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "MODELING TRANSLATION PROCESS", |
| "sec_num": "4." |
| }, |
| { |
| "text": "a) Sentence: sentence consists of an NP and a VF. VP is analyzed as a one-place predicate, which constructs a proposition out of an individual referred Co by the subject.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Translating Basic Constructions of English", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "VP is further decomposed into intransitive verb or cranaltive verb + object.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Translating Basic Constructions of English", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "Intransitive verbs and transitive verbs ere analyzed as one-place predicates and two-place predicate, respectively.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Translating Basic Constructions of English", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "One-place predicate and two-place predicate are assigned a CFSF function which generates a sentence ouc of an individual and chat which generates a sentence out of a pair of individuals, respectively.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Translating Basic Constructions of English", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "Thus, a transitive verb \"constructs\" is assigned a CPSF form:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Translating Basic Constructions of English", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "(LAMBDA (x y) (SENTENCE (\u00f7 CASE-MAR/~R (CASE=AGENT) x) (+ C~SE-MARi~R (CASE=OBJ) y) [ FRED ICATE [ VERB \"SAKUSEI-SURU\" ] J )),", |
| "eq_num": "(24)" |
| } |
| ], |
| "section": "Translating Basic Constructions of English", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "; given two individuals, this function attaches co each argument a case marker (corresponding to JOSHI or Japanese postfix) and then generates a sentence structure.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Translating Basic Constructions of English", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "The assignment (24) may be extended later to incorporate word choice mechanism.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Translating Basic Constructions of English", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "Treatment of NP in MonCague-besed semantics is significant in chat EFR expression for an NP is given a wider scope then Chat for a VP. Thus the EFR form for an ~P-VP construction looks llke:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Translating Basic Constructions of English", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "<~>(<w>),", |
| "eq_num": "(25)" |
| } |
| ], |
| "section": "Translating Basic Constructions of English", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "where <x> means EFR form for x, x=NP,... .", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Translating Basic Constructions of English", |
| "sec_num": "4.1" |
| }, |
| { |
| "text": "English quantifier which is syntactically local but semantically global.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The reason is Co provide an appropriate model for", |
| "sec_num": null |
| }, |
| { |
| "text": "For example, first order logical form for a sentence:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The reason is Co provide an appropriate model for", |
| "sec_num": null |
| }, |
| { |
| "text": "\"this command needs no operand\" (267 looks Like:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The reason is Co provide an appropriate model for", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "nor(there-exists x [needs(\"chis-command\",x) & operand(x)]),", |
| "eq_num": "(27)" |
| } |
| ], |
| "section": "The reason is Co provide an appropriate model for", |
| "sec_num": null |
| }, |
| { |
| "text": "where operator \"not\", which comes from a determiner \"no\", is given a wider scope than \"needs\". This translation is straightforward in our model; the following EFR is extracted from (26):", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The reason is Co provide an appropriate model for", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "(this(round)) Ax[(no(operand))(ly[needs(x,y)])]).", |
| "eq_num": "(28)" |
| } |
| ], |
| "section": "The reason is Co provide an appropriate model for", |
| "sec_num": null |
| }, |
| { |
| "text": "[f we make appropriate assignment including:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The reason is Co provide an appropriate model for", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "no <= (LAMBDA (p) (LAMBDA (q) \"nor(there exists x [p(x) & q(x)])\")),", |
| "eq_num": "(29)" |
| } |
| ], |
| "section": "The reason is Co provide an appropriate model for", |
| "sec_num": null |
| }, |
| { |
| "text": "we can get (27) from (28).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "The reason is Co provide an appropriate model for", |
| "sec_num": null |
| }, |
| { |
| "text": "In Engllsh-Japanese -,-'chine translation, this treatment gives an elegant solution to the :ranalation of prenominal negation, partial negation, etc.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "161", |
| "sec_num": null |
| }, |
| { |
| "text": "Since Japanese language does not have a synCactlc device for prenominal negation, \"no\" must be translated into asainly two separate constituents: one is a RENTAISHI (Japanese decerminer) and another is an auxiliary verb of negation.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "161", |
| "sec_num": null |
| }, |
| { |
| "text": "One possible assignment of CFSF looks like: ...)).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "161", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "no <= (LAMBDA (p) (U~NgDA (q)", |
| "eq_num": "(" |
| } |
| ], |
| "section": "161", |
| "sec_num": null |
| }, |
| { |
| "text": "By <MOD\u00a3FIER> we mean modification to noun by adjectives, prepositional phrases, infinitives, present/past particles, etc. The translation process is determined by a CPSF assigned co <DET>, En cases of \"the\" or \"a/an\", translation process is abic complicated. Et is almost the same as the process described in detail in section 3: firstly the <MODIFIER>s and <NOUN> are applied Co an individual like \"the chinE\" (the) or \"some-chinE\" (a/an) and a sentence will be obtained; then a noun structure is extracted and appropriate RENTAISHI or Japanese determiner is attached. c) Other cases: some ocher cases are illustrated by examples in Fig.3 .", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 635, |
| "end": 640, |
| "text": "Fig.3", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "161", |
| "sec_num": null |
| }, |
| { |
| "text": "\u2022 In order to obtain high quality translation, word choice .~chanism must be incorporated at least for handling the cases like: i) subordinate clause:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "4.2\"Word Choice Mechanism", |
| "sec_num": null |
| }, |
| { |
| "text": "\"When SI, S2\" & (when (<SI >) ) (<$2>) \"TOKI\" [$I] [[SI] \"TOKI 's] [$2] [[Sl] \"TOKI\" [S2]]", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "4.2\"Word Choice Mechanism", |
| "sec_num": null |
| }, |
| { |
| "text": "2) tense, aspect, modal: \"I bought a car\" did(<I buy a car>) \"TA\" \"WATASHI-WA JIDOUSHA-WO KAU\" \"WATASHI-WA JIDOUSHA-WO KAU TA\" ; indirect question is generated first, then it is transformed into a sentence. Construction. <x>, {x}, [x] and \"x\" stand for EFR for x, CPSF for x, CPS for x, and CPB for Japanese string x, respectively. verb in accordance with its object or its agent, adjective-noun, adverb-verb, and preposition.", |
| "cite_spans": [ |
| { |
| "start": 207, |
| "end": 234, |
| "text": "Construction. <x>, {x}, [x]", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "4.2\"Word Choice Mechanism", |
| "sec_num": null |
| }, |
| { |
| "text": "Word choice is partially solved in the analysis phase as a word meaning disambiguation.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "So the design problem [s to determine to what degree word sense is disamblguated in the analysis phase and what kind of ambiguities is left until transfer-generation phase.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "Suppose we are to translate a given preposition.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "The occurence of a preposition [s classified as:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "(a) when it is governed by verbs or nouns:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "(a-l) when governmant is strong: e.g., study on, belong to, provide for; (a-2) when govern.ment is weak: e.g., buy ... at store; (b) otherwise:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "(b-I) idiomatic: e.g., in particular, in addition; (b-2) related to its object: e.g., by bus, with high probability, without\u00f7ING.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "We treat (a) and (b-l) as an analysis problem and handle them in the analysis phase. (b-2) is more difficult and is treated in the transfergeneration phase where partial semantic interpretation [s done.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "Word choice in transfer-generatlon phase is done by using, conditional expression and attributive information included in CPS. For example, a transitive verb \"develop\" is translated differently according to its object: develop ~ (* system) ... KAINATSU-SURU t (+ film) GENZOU-SURU.", |
| "cite_spans": [ |
| { |
| "start": 260, |
| "end": 268, |
| "text": "(+ film)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "The following assignment of CPSF makes this choice poss ib le :", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "deve lop <= (LAMBDA (x y) [(CLASS y)=SYSTEM -> (\"x-GA y-WO KAIHATSU-SURU\"} ; (CLASS y)-FILM ->", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "(\"x-GA y-WO GENZOU-SURU\"};", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "EQUATION", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [ |
| { |
| "start": 0, |
| "end": 8, |
| "text": "EQUATION", |
| "ref_id": "EQREF", |
| "raw_str": "\u2022 .. ]),", |
| "eq_num": "(35)" |
| } |
| ], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "operating-syStem <-[NOUN \"OS\" with CLASS-system; ... ], (36) film <-[NOUN \"FUILUMU\" with CLASS-film; ... 1.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "To make this type of processing possible in the cases where the deep object is moved from surface In EFR level, lambda variable x is explicitly used as a place holder for the gap.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "A functor \"which\" dominates both the EFR for the embedded sentence and that for the head noun. A CPSF assigned to the functor \"which\" sends conceptual information of the head noun to the gap as follows: firstly it creates a null NF out of the head noun, then the null NP is substituted into the lambda variable for the gap.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "In word choice or semantic based translation in general, various kinds of transformations are carried out on target language structure. For example,", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "her arrival makes him happy, We have constructed a prototype system.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "It is slmplified then practical system in:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "-it has only limited vocabulary, Sample texts are taken from real computer manuals or abstracts of computer journals. Initially, four sample texts (40 sentences) are chosen. Currently it is extended to I0 texts (72 sentences).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "Additional features are introduced Ln order to make the system more practical. a) Parser: declarative rules are inefficient for dealing with sentences in real cexts. The parser uses production type rules each of which is classified according to its invocation condition.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "Declarative rules are manually converted into this rule type. b) Automatic postedicor: transfer process defined so far concentrates on local processings. Even if certain kinds of ambiguities are resolved in this phase, there still remains a possibility that new ambiguity is introduced in generation phase. Instead of incorporating into the transfer-generation phase a sophisticated mechanism for filtering out ambiguities, we attach a postprocessor which will \"reform\" a phrase structure yielding ambiguous output.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "Treetree transformation rules are utilized here.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "Current result of our machine cransLacion system is shown in Appendix.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Fig.3. Examples of Translation of Basic English", |
| "sec_num": null |
| }, |
| { |
| "text": "Translation of a Sample Text. }((h\u00a2.*ne: ,, a %~qem (or IOcai communlcat,on among computing statiOns Our experlmcn[ai E.thcrnc; u~;.: ~ppcc coaxial eabl~ Io c~rn ~urlaoie-len~th dlgltal data packets among, for example, pcrsonai minicomputers, pr~nung f'aciliues, iar~\u00a2 ~ie s~orage de,.~ces, magnetic r~pe backup stauons.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "APPENDIX:", |
| "sec_num": null |
| }, |
| { |
| "text": "lar~er cenlra! computers, and longer-haul communlcauor~ equzpment.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "APPENDIX:", |
| "sec_num": null |
| }, |
| { |
| "text": "The ,~hared communicauon facilit.~, a branchm8 E~er. ~s passive. A sIauons E~heme~ interface connecL~ b,-sonalb through an interface cabie to a Lranscezver which in turn ~ps mLo the passing F/her 4 packet is hmadcas{ onto the F:'ther. is heard b.~ all smr/ons, and is cop~ed from the Er.her b.~ desunauons ~.hich soiL'c: ~! accorain~ to the packe:s leadm8 address bits. This ,s 0madc.~l packe: s~tching alld shouic be disunguzshec~ from s(ore-and-t'or~ard packe( switchin 8 m wh,ch muun9 ~ nerformed h~ mtermedmte pruccssm~ elements. To handle {he demand~ of ~,rowth. an F/heine! can be ex~ended usm@ packet repeaters (or signaJ regeneration, packe{ filters t'or crar~c locaJzzauon, an(~ p~ket gate~a.vs /'or intcmetwurk address extension.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "APPENDIX:", |
| "sec_num": null |
| }, |
| { |
| "text": "Control is completeb dnstrioutea among stauons with packet transmissions coordinated ',nmugh ", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "APPENDIX:", |
| "sec_num": null |
| } |
| ], |
| "back_matter": [ |
| { |
| "text": "Having completed initial experiments, it is shown that our framework is applicable to real texts under plausible assumption. The prototype system has a clear architecture.Central rule interpreter contains no complicated parts. Although several errors occured in the implementation of translation rules, they were easily detected and eliminated for the sake of data flow property. 2) Logic vs machine translation: The sentence (44) is logically equivalent to (45), but that paraphrasing is bad in machine translation.he reads and writes English. (44) he reads English and he writes English. 457. CONCLUSION Application of formal semantics to machine translation brings about new phase of machine translation.It makes the translation process clearer than conventional systems. The theory has been tested by implementing a prototype, which can translate real texts with plausible human assist.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "DISCUSSION", |
| "sec_num": "6." |
| } |
| ], |
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| "num": null, |
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| "text": "In general, correspondence of ~P and individual is indirect in EFR. The association of an NF with its referent x is indicated as follows:<~>(Ix{ ... x ... ;).i',enCence type one-place predlcaCe type ; <NP> stands for EFR expression for NP.(31)Most of ocher NP's correspond co ice referent more directly.The application rule reflecting this fact is:[NFJ([O~-eU~CE-PREDI) -[ONE-PU~CE-FREOI([NP*]),(32)where, ix] stands for a CPS for x.b) Internal structure of NP: the below illustrates the structure of EFR expression assigned CO an NP: <DET>(<MOD[FIER>(...(<MDDIFIER>(<NOUN>))" |
| }, |
| "FIGREF2": { |
| "type_str": "figure", |
| "num": null, |
| "uris": null, |
| "text": "KATTA3) passive:\" ... is broken ... \" & ... en(break) ...C~x~ ,,.GA,, y ,,.WO KOWASU ,WA DONO-JIDOUSHA-WO MOTSU-KA\"" |
| }, |
| "FIGREF3": { |
| "type_str": "figure", |
| "num": null, |
| "uris": null, |
| "text": "object position by transformations, link information between verb and its (deep) object should be represented explicitly. The below shows bow it is done in the case of relative clause. Phrase Structu~ (for restrictive use): NP (which(Xx[ .. x ... ]))(<noun>) link from head noun to place holder" |
| }, |
| "FIGREF4": { |
| "type_str": "figure", |
| "num": null, |
| "uris": null, |
| "text": "interactive disembiguation is done instead of automatic disambiguaCion, and -word choice mmchenism is limited to typical cases since overall definition of rules have not yet been completed." |
| }, |
| "FIGREF5": { |
| "type_str": "figure", |
| "num": null, |
| "uris": null, |
| "text": "sr, austical arbitration. Transmissions inl~ated b) a s~aoon defer ~o an)' which may' alread.~ be m progress. Once s~arted, if interference v,.:d~ ocher packe~ ~s detected, a transmission ts aborted and reschedu[ed b;, ~LS source s~auon. A~er a certain period of interference-tree transmission, a packet is heard b.v all s[aoons and will run to completion without interference. E~ernet controllers m colliding sauons each generate random retransrniss~on inten-ab to avoid repeated co[iismns. The mean of a packer's retransmission inter, aiS is adjusted as a f~ncUon of co]hsion histon. to keep Ether uulizauon near ~le opumum v.-,.h changing network load. E~en ~,nen transmuted w~thout source-detected interference, a packet may still not reach z~ destination w~thouz error: thus. packets are delivered only ~.~th high probabilio'. Scauons requmng a residual error rate lower than thai provided b.~ the bare Ethemet packet transport mechanism muSl follo~ mutually agreed upon packet protOcols. ~ U -9\"o~'~. ~ll~'-~,'~':, ~' \",\" J'2\"x~ = -~ 3 I%~ ='r ~ ~, ~-n,,~---~a, ~ ;~o 097-xx9 = -:~ ~ > C2 o (ff.~I~1~T ~ 6 ~, ~7.~ \">'_~ -~ ~ ~-.3, ~;o'~?;~, & ~ i v --x~T~,~/~:~ L C,~ \"~ ~ .9,'f~.~ ~ ~.\" L I~'<. ~ ~ ~ ~.r.-~ --.~' : t~ ;~.. ~ \u00a2)l~\u00a2) E THI-= RNET, ~'r v b ~I~/~IL \" \u2022 --, Tt~f~ ~ tl \"5 L er)j: ;) 'L ~\" ,~f~.~r) [65" |
| }, |
| "TABREF0": { |
| "num": null, |
| "type_str": "table", |
| "content": "<table><tr><td/><td colspan=\"2\">LEXICAL difference</td></tr><tr><td/><td/><td><E></td><td><J></td></tr><tr><td/><td colspan=\"2\">translate</td><td>HONYAKU SURU</td></tr><tr><td/><td colspan=\"2\">interpret ~ ~</td><td>KAISHAKU SURU</td></tr><tr><td/><td colspan=\"2\">understand</td><td>RIKAI SURU</td></tr><tr><td/><td/><td>grasp /</td><td>TSUKAMU</td></tr><tr><td/><td/><td>hold ~</td><td>TAMOTSU</td></tr><tr><td/><td/><td>keep</td><td>MAMORU</td></tr><tr><td/><td/><td>.,,</td></tr><tr><td/><td colspan=\"3\">CONCEPTUAL difference</td></tr><tr><td/><td colspan=\"3\"><E2 her arrival makes him happy</td></tr><tr><td/><td>~..</td><td>paraphrasing</td><td>[s needed</td></tr><tr><td/><td colspan=\"2\"><j> KARE</td></tr><tr><td><E>: NP\u00f7POSTMODIF[ER:</td><td>an apple on the box,</td><td/></tr><tr><td colspan=\"2\"><J>: PRENOMINAL MODIFIER+NP: HAKO NO UE NO RINGO</td><td/></tr></table>", |
| "html": null, |
| "text": "WA KANOJO GA TOUCHAKU SHITA NODE URESHII. (he becomes happy because she has arrived) Fig.l. Examples of Differences between English and Japanese. <E>: English; <J>: Japanese. b) Ability of representing semantic relations: In English-Japanese translation," |
| }, |
| "TABREF2": { |
| "num": null, |
| "type_str": "table", |
| "content": "<table><tr><td>not )</td><td>always</td><td colspan=\"2\">( he</td><td>(</td><td>late</td><td>(</td><td>comes</td><td>) ) ) )</td><td>..</td><td>EFR</td><td>..</td></tr><tr><td/><td colspan=\"2\">lq, kp</td><td colspan=\"6\">[ p< ~l l IrF+V I A~V ,rll,l x I N[ p4 P IIII L IJl L itS \\ Z.a' \u00a2# z comes</td><td/><td/></tr><tr><td/><td/><td/><td/><td/><td/><td colspan=\"2\">.A.</td><td/><td/><td/></tr><tr><td/><td>S</td><td/><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"2\">iv) Transformations:</td><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"4\">e.g., (? TENSE (TENSE-PAST~ x).</td><td/><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"6\">indicator; operator-name; PARAMs; argumen~</td></tr><tr><td/><td/><td/><td/><td/><td colspan=\"3\">v) CPS construction:</td><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"5\">e.g., <SENTENCE (x y) with ... 7.</td><td/></tr><tr><td/><td/><td/><td/><td/><td/><td>/</td><td>\\</td><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/><td/><td colspan=\"3\">new category; descendents</td><td/><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"2\">vi) Conditionals:</td><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"5\">[ <condition> I -> <CPSF>I; ... ].</td><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"2\">vii) Lambda form:</td><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"5\">e.g., (LAMBDA (x) (+ PASSIVE () x))</td><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"6\">Using those description tools, translation</td></tr><tr><td/><td/><td/><td/><td colspan=\"6\">process is modeled as a three staged process:</td><td/></tr><tr><td/><td>3. FORMAL TOOLS</td><td/><td/><td/><td colspan=\"4\">stage I (analysis): anlyzes English</td><td/><td/></tr><tr><td/><td/><td/><td/><td/><td colspan=\"4\">sentence and extracts EFR form,</td><td/><td/></tr><tr><td colspan=\"4\">Formal description tools have been developed</td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td colspan=\"4\">co provide a precise description of the idea men-</td><td/><td colspan=\"5\">stage 2 (transfer): substitutes CPSF to</td><td/></tr><tr><td colspan=\"2\">tioned Ln the last section.</td><td/><td/><td/><td colspan=\"4\">each lexical item in the EFR form,</td><td/><td/></tr></table>", |
| "html": null, |
| "text": "+NEGHe does not always cOme late." |
| } |
| } |
| } |
| } |
