| { |
| "paper_id": "P82-1028", |
| "header": { |
| "generated_with": "S2ORC 1.0.0", |
| "date_generated": "2023-01-19T09:16:20.581584Z" |
| }, |
| "title": "THE TEXT SYSTEM FO~NATURAL LANGUAGE GENERATION: AN OVERVIEW*", |
| "authors": [ |
| { |
| "first": ":", |
| "middle": [], |
| "last": "Keown", |
| "suffix": "", |
| "affiliation": { |
| "laboratory": "", |
| "institution": "The Moore School University of Pennsylvania Philadelphia", |
| "location": { |
| "postCode": "19104", |
| "settlement": "Pa" |
| } |
| }, |
| "email": "" |
| } |
| ], |
| "year": "", |
| "venue": null, |
| "identifiers": {}, |
| "abstract": "Computer-based generation of natural language requires consideration of two different types of problems: i) determining the content and textual shape of what is to be said, and 2) transforming that message into English. A computational solution to the problems of deciding what to say and how to organize it effectively is proposed that relies on an interaction between structural and semantic processes. Schemas, which encode aspects of discourse structure, are used to guide the generation process. A focusing mechanism monitors the use of the schemas, providing constraints on what can be said at any point. These mechanisms have been implemented as part of a generation method within the context of a natural language database system, addressing the specific problem of responding to questions about database structure.", |
| "pdf_parse": { |
| "paper_id": "P82-1028", |
| "_pdf_hash": "", |
| "abstract": [ |
| { |
| "text": "Computer-based generation of natural language requires consideration of two different types of problems: i) determining the content and textual shape of what is to be said, and 2) transforming that message into English. A computational solution to the problems of deciding what to say and how to organize it effectively is proposed that relies on an interaction between structural and semantic processes. Schemas, which encode aspects of discourse structure, are used to guide the generation process. A focusing mechanism monitors the use of the schemas, providing constraints on what can be said at any point. These mechanisms have been implemented as part of a generation method within the context of a natural language database system, addressing the specific problem of responding to questions about database structure.", |
| "cite_spans": [], |
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| "eq_spans": [], |
| "section": "Abstract", |
| "sec_num": null |
| } |
| ], |
| "body_text": [ |
| { |
| "text": "Deciding what to say and how to organize it effectively are two issues of particular importance to the generation of natural language text.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "INTRODUCTION", |
| "sec_num": "1.0" |
| }, |
| { |
| "text": "In the past, researchers have concentrated on local issues concerning the syntactic and lexical choices involved in transforming a pre-determined message into natural language. The research described here ~nphasizes a computational Solution to the more global problems of determining the content and textual shape of what is to be said. ~re specifically, my goals have been the development and application of principles of discourse structure, discourse coherency, and relevancy criterion to the computer generation of text. These principles have been realized in the TEXT system, reported on in this paper.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "INTRODUCTION", |
| "sec_num": "1.0" |
| }, |
| { |
| "text": "The main features of the generation method used in TEXT include I) an ability to select relevant information, 2) a system for pairing rhetorical techniques (such as analogy) with discourse purv~ses (such as defining terms) and 3) a focusing mec~mnism. Rhetorical techniques, which encode aspects of discourse structure, guide the selection of information for inclusion in the text from a relevant knowledge poq~l -a subset of *This work was partially supported by National Science ~Dundation grant #MCS81-07290.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "INTRODUCTION", |
| "sec_num": "1.0" |
| }, |
| { |
| "text": "the knowledge base which contains information relevant to the discourse purpose.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "INTRODUCTION", |
| "sec_num": "1.0" |
| }, |
| { |
| "text": "The focusing mechanism helps maintain discourse coherency.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "INTRODUCTION", |
| "sec_num": "1.0" |
| }, |
| { |
| "text": "It aids in the organization of the message by constraining the selection of information to be talked about next to that which ties in with the previous discourse in an appropriate way. These processes are described in more detail after setting out the framework of the system.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "INTRODUCTION", |
| "sec_num": "1.0" |
| }, |
| { |
| "text": "In order to test generation principles, the TEXT system was developed as part of a natural language interface to a database system, addressing the specific problem of generating answers to questions about database structure. Three classes of questions have been considered: questions about information available in the database, requests for definitions, and questions about the differences between database entities [MCKE(3WN 80] .", |
| "cite_spans": [ |
| { |
| "start": 417, |
| "end": 430, |
| "text": "[MCKE(3WN 80]", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "APPLICATION", |
| "sec_num": "2.0" |
| }, |
| { |
| "text": "In this context, input questions provide the initial motivation for speaking.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "APPLICATION", |
| "sec_num": "2.0" |
| }, |
| { |
| "text": "the specific application of answering questions about database structure was used primarily for testing principles about text generation, it is a feature that many users of such systems would like.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Although", |
| "sec_num": null |
| }, |
| { |
| "text": "Several experiments ([MALHOTRA 75] , [TENNANT 79] ) have shown that users often ask questions to familiarize themselves with the database structure before proceeding to make requests about the database contents.", |
| "cite_spans": [ |
| { |
| "start": 20, |
| "end": 34, |
| "text": "([MALHOTRA 75]", |
| "ref_id": null |
| }, |
| { |
| "start": 37, |
| "end": 49, |
| "text": "[TENNANT 79]", |
| "ref_id": null |
| } |
| ], |
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| "eq_spans": [], |
| "section": "Although", |
| "sec_num": null |
| }, |
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| "text": "The three classes of questions considered for this system were among those shown to be needed in a natural language database system. Implementation of the TEXT system for natural language generation used a portion of the Office of Naval Research (ONR) database containing information about vehicles and destructive devices. Some examples of questions that can be asked of the system include:", |
| "cite_spans": [], |
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| "section": "Although", |
| "sec_num": null |
| }, |
| { |
| "text": "> What is a frigate? > What do you know about submarines? > What is the difference between a and a kitty hawk?", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Although", |
| "sec_num": null |
| }, |
| { |
| "text": "whisky", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Although", |
| "sec_num": null |
| }, |
| { |
| "text": "The kind of generation of which the system is capable is illustrated by the response it generates to question (A) below.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Although", |
| "sec_num": null |
| }, |
| { |
| "text": "All entities in the (INR database have DB attributes R~MARKS.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A) ~at kind of data do you have?", |
| "sec_num": null |
| }, |
| { |
| "text": "There are 2 types of entities in the ONR database: destructive devices and vehicles.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A) ~at kind of data do you have?", |
| "sec_num": null |
| }, |
| { |
| "text": "The vehicle has DB attributes that provide information on SPEED-INDICES and TRAVEL-MEANS. The destructive device has DB attributes that provide information on LETHAL-INDICES.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A) ~at kind of data do you have?", |
| "sec_num": null |
| }, |
| { |
| "text": "TEXT does not itself contain a facility for interpreting a user's questions. Questions must be phrased using a simple functional notation (shown below) which corresponds to the types of questions that can be asked . It is assumed that a component could be built to perform this type of task and that the decisions it must make would not affect the performance of the generation system.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "A) ~at kind of data do you have?", |
| "sec_num": null |
| }, |
| { |
| "text": "where <e>, <el>, <e2> represent entities in the database.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "I. (definition <e>) 2. (information <e>) 3. (differense <el> <e2>)", |
| "sec_num": null |
| }, |
| { |
| "text": "In answer ing a question about database structure, TEXT identifies those rhetorical techniques that could be used for presenting an appropriate answer.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "SYSTEM OVERVIEW", |
| "sec_num": "3.0" |
| }, |
| { |
| "text": "On the basis of the input question, semantic processes produce a relevant knowledge pool. A characterization of the information in this pool is then used to select a single partially ordered set of rhetorical techniques from the various possibilities.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "SYSTEM OVERVIEW", |
| "sec_num": "3.0" |
| }, |
| { |
| "text": "A formal representation of the answer (called a \"message\" ) is constructed by selecting propositions from the relevant knowledge pool which match the rhetorical techniques in the given set. The focusing mechanism monitors the matching process; where there are choices for what to say next (i.e.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "SYSTEM OVERVIEW", |
| "sec_num": "3.0" |
| }, |
| { |
| "text": "-either alternative techniques are possible or a single tec~mique matches several propositions in the knowledge pool), the focusing mechanism selects that proposition which ties in most closely with the previous discourse. Once the message has been constructed, the system passes the message to a tactical component [BOSSIE 81 ] which uses a functional grammar [KAY 79] to translate the message into English.", |
| "cite_spans": [ |
| { |
| "start": 316, |
| "end": 326, |
| "text": "[BOSSIE 81", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "SYSTEM OVERVIEW", |
| "sec_num": "3.0" |
| }, |
| { |
| "text": "Answering questions about the structure of the database requires access to a high-level description of the classes of objects ino the database, their properties, and the relationships between them. The knowledge base used for the TEXT system is a standard database model which draws primarily from representations developed by Chen [CHEN 76] course of an answer.", |
| "cite_spans": [ |
| { |
| "start": 327, |
| "end": 341, |
| "text": "Chen [CHEN 76]", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "The relevant knowledge pool is constructed by a fairly simple process. For requests for definitions or available information, the area around the questioned object containing the information immediately associated with the entity (e.g. its superordinates, sub-types, and attributes) is circumscribed and partitioned from the remainir~ knowledge base. For questions about tk~ difference between entities, the information included in the relevant knowledge pool depends on how close in the generalization hierarchy t~ two entities are. For entities that are very similar, detailed attributive information is included. For entities that are very different, only generic class information is included. A combination of this information is included for entities falling between t~se two extremes.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "(See [MCKEOWN 82] for further details).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "6.0 R~LETORICAL PREDICATES ~%etorical predicates are the means which a speaker has for describing information. ~hey characterize the different types of predicating acts s/he may use and delineate the structural relation between propositions in a text. some examples are \"analogy\" (comparison with a familiar object), \"constituency\" (description of sub-parts or sub-types), and \"attributive\" (associating properties with an entity or event).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "Linguistic discussion of such predicates (e.g. [GRIMES 75] , [SHEPHERD 26]) indicates that some combinations are preferable to others. Moreover, Grimes claims that predicates are recursive and can be used to identify the organization of text on any level (i.e.", |
| "cite_spans": [ |
| { |
| "start": 47, |
| "end": 58, |
| "text": "[GRIMES 75]", |
| "ref_id": null |
| }, |
| { |
| "start": 61, |
| "end": 75, |
| "text": "[SHEPHERD 26])", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "-proposition, sentence, paragraph, or longer sequence of text), alti~ugh he does not show how.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "I have examined texts and transcripts and have found that not only are certain combinations of rhetorical tec~miques more likely than others, certain ones are more appropriate in some discourse situations than others. For example, I found that objects were frequently defined by employing same combination of the following means: (i) identifying an item as a memDer of some generic class,", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "describing an object's function, attributes, and constituency (either physical or class),", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "(3) making analogies to familiar objects, and (4) providing examples. These techniques were rarely used in random order; for instance, it was common to identify an item as a member of some generic class before providing examples.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "In the TEXT system, these types of standard patterns of discourse structure have been captured in schemas associated with explicit discourse purposes.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "The schemas loosely identify normal patterns of usage. The~ are not intended to serve as grammars of text.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "The schema shown be-~ ~rves the purposes o~ providing definitions:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "Identification Schema identification (class&attribute/function) [analogy~constituency~attributive]* [particular-illustration~evidence]+ {amplification~analogy~attributive} {particular-illustration/evidence}", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "Here, \"{ ]\" indicates optionality, \"/\" indicates alternatives, \"+\" indicates that the item may appear l-n times, and \"*\" indicates that the item may appear O-n times. The order of the predicates indicates that the normal pattern of definitions is an identifying pro~'~tion followed by any number of descriptive predicates.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "The speaker then provides one or more examples and can optionally close with some additional descriptive information and possibly another example.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "TEXT's response to the question \"What is a ship?\" (shown below) was generated using the identification schema. ~e sentences are numbered to show the correspondence between each sentence and the predicate it corresponds to in the instantiated schema (tile numbers do not occur in the actual output). TEXT'S response to the question \"What do you know about vehicles?\" was generated using the constituency schema. It is shown below along with the predicates that were instantiated for the answer. 2) qhere are 2-types of vehicl~s in the ONR data~]se: aircraft and water-going vehicles.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "3) The water-going vehicle has DB attributes that provide information on TRAVEL MEANS and WATER GOING OPERATION. 4) The ~ircraft has DB \u00b0 attributes --that provide information on TRAVEL MEANSf FLIGHT RADIUS, CEILING and ROLE. Other DB attributes -of the vehicle include FUEL( FUEL_CAP~EITY and FUEL_TYPE) and FLAG.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "Two other strategies were identified in the texts examined. These are encoded in the attributive schema, which is used to provide detailed information about a particular aspect of an entity, and the compar e and contrast schema, which encodes a strategy --~r contrasting two entities using a description of their similarities and their differences.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "For more detail on these strategies, see [MCKEGWN 82 ].", |
| "cite_spans": [ |
| { |
| "start": 41, |
| "end": 52, |
| "text": "[MCKEGWN 82", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "KNOWLEDGE BASE", |
| "sec_num": "4.0" |
| }, |
| { |
| "text": "As noted earlier, an examination of texts revealed that different strategies were used in different situations.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "USE OF THE SCHEMAS", |
| "sec_num": "7.0" |
| }, |
| { |
| "text": "In TEXT, this association of technique with discourse purpose is achieved by associating the different schemas with different question-types.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "USE OF THE SCHEMAS", |
| "sec_num": "7.0" |
| }, |
| { |
| "text": "For example, if the question involves defining a term, a different set of schemas (and therefore rhetorical techniques) is chosen than if the question involves describing the type of information available in the database.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "USE OF THE SCHEMAS", |
| "sec_num": "7.0" |
| }, |
| { |
| "text": "The identification schema can be used in response to a request for a definition.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "USE OF THE SCHEMAS", |
| "sec_num": "7.0" |
| }, |
| { |
| "text": "The purpose of the attributive schema is to provide detailed information about one particular aspect of any concept and it can therefore be used in response to a request for information. In situations where an object or concept can be described in terms of its sub-parts or sub-classes, the constituency schema is used. It may be selected in response to requests for either definitions or information.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "USE OF THE SCHEMAS", |
| "sec_num": "7.0" |
| }, |
| { |
| "text": "The compare and contrast schema is used in response ~o a questl'i'~ about the difference between objects.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "USE OF THE SCHEMAS", |
| "sec_num": "7.0" |
| }, |
| { |
| "text": "A surmary of the assignment of schemas to question-types is shown in Figure 2 .", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 69, |
| "end": 77, |
| "text": "Figure 2", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "USE OF THE SCHEMAS", |
| "sec_num": "7.0" |
| }, |
| { |
| "text": "Schemas used for TEXT i.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "USE OF THE SCHEMAS", |
| "sec_num": "7.0" |
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| "text": "3. Once a question has been posed to TEXT, a schema must be selected for the response structure which will then be used to control the decisions involved in deciding what to say when. On the basis of the given question, a set of schemas is selected as possible structures for the response. This set includes those sch~nas associated with the given question-type (see Figure 2 above) . A single schema is selected out of this set on the basis of the information available to answer the question.", |
| "cite_spans": [], |
| "ref_spans": [ |
| { |
| "start": 367, |
| "end": 382, |
| "text": "Figure 2 above)", |
| "ref_id": null |
| } |
| ], |
| "eq_spans": [], |
| "section": "2.", |
| "sec_num": null |
| }, |
| { |
| "text": "For example, in response to requests for definitions, the constituency schema is selected when the relevant knowledge pool contains a rich description of the questioned object's sub-classes and less information about the object itself. When this is not the case, the identification schema is used.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "4.", |
| "sec_num": null |
| }, |
| { |
| "text": "The test for what kind of information is available is a relatively simple one. If the questioned object occurs at a higher level in the hierarchy than a pre-determined level, the constituency schema is used. Note that the higher an entity occurs in the hierarchy, the less descriptive information is available about the entity itself. More information is available about its sub-parts since fewer common features are associated with entities higher in the hierarchy.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "4.", |
| "sec_num": null |
| }, |
| { |
| "text": "This type of semantic and structural interaction means that a different schema may be used for answering the same type of question. An earlier example showed that the identification schema was selected by the TEXT system in response to a request for a definition of a ship. In response to a request for a definition of a guided projectile (shown below), the constituency schema is selected since more information is available about the sub-classes of the guided projectile than about the guided projectile itself.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "4.", |
| "sec_num": null |
| }, |
| { |
| "text": "Schema selected: Constituency i) identification 2) constituency 3) identification 4) identification 5) evidence 6) evidence 7) attributive I) A guided projectile is a projectile that is self-propelled. 2) There are 2 types of guided projectiles in the ONR database: torpedoes and missiles. 3) The missile has a target location in the air or on the earth's surface.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "(definition GUIDED)", |
| "sec_num": null |
| }, |
| { |
| "text": "4) The torpedo has an underwater target location.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "(definition GUIDED)", |
| "sec_num": null |
| }, |
| { |
| "text": "5 Once a schema has been selected, it is filled by matching the predicates it contains against the relevant knowledge pool. The semantics of each predicate define the type of information it can match in the knowledge pool.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "(definition GUIDED)", |
| "sec_num": null |
| }, |
| { |
| "text": "The semantics defined for TEXT are particular to the database query dumain and would have to be redefined if the schemas were to be used in another type of system (such as a tutorial system, for example).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "(definition GUIDED)", |
| "sec_num": null |
| }, |
| { |
| "text": "The semantics are not particular, however, to the domain of the database. When transferring the system from one database to another, the predicate semantics would not have to be altered.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "(definition GUIDED)", |
| "sec_num": null |
| }, |
| { |
| "text": "A proposition is an instantiated predicate; predicate arguments have been filled with values from the knowledge base. An instantiation of the identification predicate is shown below along with its eventual translation. The schema is filled by stepping through it, using the predicate s~nantics to select information which matches the predicate arguments. In places where alternative predicates occur in the schema, all alternatives are matched against the relevant knowledge pool producing a set of propositions. The focus constraints are used to select the most appropriate proposition.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "(definition GUIDED)", |
| "sec_num": null |
| }, |
| { |
| "text": "The schemas were implemented using a formalism similar to an augmented transition network (ATN). Taking an arc corresponds to the selection of a proposition for the answer. States correspond to filled stages of the schema. The main difference between the TEXT system implementation and a usual ATN, however, is in the control of alternatives.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "(definition GUIDED)", |
| "sec_num": null |
| }, |
| { |
| "text": "Instead of uncontrolled backtracking, TEXT uses one state lookahead. From a given state, it explores all possible next states and chooses among them using a function that encodes the focus constraints. This use of one state lookahead increases the efficiency of the strategic component since it eliminates unbounded non-determinism.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "(definition GUIDED)", |
| "sec_num": null |
| }, |
| { |
| "text": "So far, a speaker has been shown to be limited in many ways.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "For example, s/he is limited by the goal s/he is trying to achieve in the current speech act. TEXT's goal is to answer the user's current question.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "To achieve that goal, the speaker has limited his/her scope of attention to a set of objects relevant to this goal, as represented by global focus or the relevant knowledge pool.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "The speaker is also limited by his/her higher-level plan of how to achieve the goal.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "In TEXT, this plan is the chosen schema. Within these constraints, however, a speaker may still run into the problem of deciding what to say next.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "A focusing mechanism is used to provide further constraints on what can be said. The focus constraints used in TEXT are immediate, since they use the most recent proposition (corresponding to a sentence in the ~glish answer) to constrain the next utterance. Thus, as the text is constructed, it is used to constrain what can be said next.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "Sidner [SIDNER 79 ] used three pieces of information for tracking immediate focus: the immediate focus of a sentence (represented by the current focus -CF), the elements of a sentence ~---I~hare potential candidates for a change in focus (represented by a potential focus list -PFL), and past immediate focY [re--pr--esent--'-~--6y a focus stack).", |
| "cite_spans": [ |
| { |
| "start": 7, |
| "end": 17, |
| "text": "[SIDNER 79", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "She showed that a speaker has the 3~6~win-g'~tions from one sentence to the next: i) to continue focusing on the same thing, 2) to focus on one of the items introduced in the last sentence, 3) to return to a previous topic in ~lich case the focus stack is popped, or 4) to focus on an item implicitly related to any of these three options. Sidner's work on focusing concerned the inter~[e__tation of anaphora. She says nothing about which of these four options is preferred over others since in interpretation the choice has already been made.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "For generation, ~.~ver, a speaker may have to choose between these options at any point, given all that s/he wants to say. The speaker may be faced with the following choices: i) continuing to talk about the same thing (current-focus equals current-focus of the previous sentence) or starting to talk about something introduced in the last sentence (current-focus is a member of potential-focus-list of the previous sentence) and 2) continuing to talk about the same thing (current focus remains the same) or returning to a topic of previous discussion (current focus is a member of the focus-stack).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "When faced with the choice of remaining on the same topic or switching to one just introduced, I claim a speaker's preference is to switch. If the speaker has sanething to say about an item just introduced and does not present it next, s/he must go to the trouble of re-introducing it later on. If s/he does present information about the new item first, however, s/he can easily continue where s/he left off by following Sidner's legal option #3. ~qus, for reasons of efficiency, the speaker should shift focus to talk about an item just introduced when s/he has something to say about it.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "When faced with the choice of continuing to talk about the same thing or returning to a previous topic of conversation, I claim a speaker's preference is to remain on the same topic. Having at some point shifted focus to the current focus, the speaker has opened a topic for conversation. By shifting back to the earlier focus, the speaker closes this new topic, implying that s/he has nothing more to say about it when in fact, s/he does.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "Therefore, the speaker should maintain the current focus when possible in order to avoid false implication of a finished topic.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "These two guidelines for changing and maintaining focus during the process of generating language provide an ordering on the three basic legal focus moves that Sidner specifies: I.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FOCUSING MECHANISM", |
| "sec_num": "8.0" |
| }, |
| { |
| "text": "3. change focus to member of previous potential focus list if possible -CF (new sentence) is a member of PFL (last sentence) maintain focus if possible -CF (new sentence) = CF (last sentence) return to topic of previous discussion -CF (new sentence) is a member of focus-stack I have not investigated the problem of incorporating focus moves to items implicitly associated with either current loci, potential focus list members, or previous foci into this scheme. This remains a topic for future research.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2.", |
| "sec_num": null |
| }, |
| { |
| "text": "Even these guidelines, however, do not appear to be enough to ensure a connected discourse. Although a speaker may decide to focus on a specific entity, s/he may want to convey information about several properties of that entity.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2.", |
| "sec_num": null |
| }, |
| { |
| "text": "S/he will describe related properties of the entity before describing other properties.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2.", |
| "sec_num": null |
| }, |
| { |
| "text": "Thus, strands of semantic connectivity will occur at more than one level of the discourse.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2.", |
| "sec_num": null |
| }, |
| { |
| "text": "An example of this phenomenon is given in dialogues (A) and (B) below. In both, the discourse is focusing on a single entity (the balloon), but in (A) properties that must be talked about are presented randomly.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2.", |
| "sec_num": null |
| }, |
| { |
| "text": "In (B), a related set of properties (color) is discussed before the next set (size). (B), as a result, is more connected than (A).", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2.", |
| "sec_num": null |
| }, |
| { |
| "text": "(A) The balloon was red and white striped. Because this balloon was designed to carry men, it had to be large.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2.", |
| "sec_num": null |
| }, |
| { |
| "text": "It had a silver circle at the top to reflect heat. In fact, it was larger than any balloon John had ever seen.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2.", |
| "sec_num": null |
| }, |
| { |
| "text": "(B) The balloon was red and white striped. It had a silver circle at the top to reflect heat. Because this balloon was designed to carry men, it had to be large. In fact, it was larger than any balloon John had ever seen.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2.", |
| "sec_num": null |
| }, |
| { |
| "text": "In the generation process, this phenomenon is accounted for by further constraining the choice of what to talk about next to the proposition with the greatest number of links to the potential focus list.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "2.", |
| "sec_num": null |
| }, |
| { |
| "text": "TEXT uses the legal focus moves identified by Sidner by only matching schema predicates against propositions which have an argument that can be focused in satisfaction of the legal options. Thus, the matching process itself is constrained by the focus mechanism.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Use Of The Focus Constraints", |
| "sec_num": "8.1" |
| }, |
| { |
| "text": "The focus preferences developed for generation are used to select between remaining options. These options occur in TEXT when a predicate matches more than one piece of information in the relevant knowledge pool or when more ~,an one alternative in a schema can be satisfied. In such cases, the focus guidelines are used to select the most appropriate proposition. When options exist, all propositions are selected which have as focused argument a member of the previous PFL. If none exist, then whose focused current-focus. propositions are is a member of filtering steps possibilities to proposition with all propositions are selected argument is the previous If none exist, then all selected whose focused argument the focus-stack.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Use Of The Focus Constraints", |
| "sec_num": "8.1" |
| }, |
| { |
| "text": "If these do not narrow down the a single proposition, that the greatest number of links to the previous PFL is selected for the answer. Tne focus and potential focus list of each proposition is maintained and passed to the tactical component for use in selecting syntactic constructions and pronominalization.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Use Of The Focus Constraints", |
| "sec_num": "8.1" |
| }, |
| { |
| "text": "Interaction of the focus constraints with the schemas means that although the same schema may be selected for different answers, it can be instantiated\" in different ways. Recall that the identification schema was selected in response to the question \"What is a ship?\" and the four predicates, identification, evidence, attributive, and ~articular-illustrati0n, were instantiated. Tne identification schema was also selected in response to the question \"What is an aircraft carrier?\", but different predicates were instantiated as a result of the focus constraints:", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Use Of The Focus Constraints", |
| "sec_num": "8.1" |
| }, |
| { |
| "text": "(definition AIRCRAFT-CARRIER) Schema selected: identification I) identification 2) analogy 3) particular-illustration 4) amplification 5) evidence i) An aircraft carrier is a surface ship with a DISPLACEMENT between 78000 and 80800 and a LENGTH between 1039 and 1063. 2) Aircraft carriers have a greater LENGTH than all other ships and a \" greater DISPLACEMENT than most other ships. 3) Mine warfare ships, for example, have a DISPLACF24ENT of 320 and a LENGTH of 144. 4) All aircraft carriers in the ONR database have REMARKS of 0, FUEL TYPE of BNKR, FLAG of BLBL, BEAM of 252, ENDU--I~NCE RANGE of 4000, ECONOMIC SPEED of 12, ENDURANCE SPEED of 30 and PRO~LSION of STMTURGRD. 5)--A ship is classified as an aircraft carrier if the characters 1 through 2 of its HULL NO are CV.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "Use Of The Focus Constraints", |
| "sec_num": "8.1" |
| }, |
| { |
| "text": "Several possibilities for further development of the research described here include i) the use of the same strategies for responding to questions about attributes, events, and relations as well as to questions about entities, 2) investigation of strategies needed for responding to questions about the system processes (e.g. How is manufacturer ' s cost determined?) or system capabilities (e.g.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FUTURE DIRECTIONS", |
| "sec_num": "9.0" |
| }, |
| { |
| "text": "Can you handle ellipsis?) , 3) responding to presuppositional failure as well as to direct questions, and 4) the incorporation of a user model in the generation process (currently TEXT assumes a static casual, naive user and gears its responses to this characterization). Tnis last feature could be used, among other ways, in determining the amount of detail required (see [ MCKEOWN 82 ] for discussion of the recursive use of the sch~nas).", |
| "cite_spans": [ |
| { |
| "start": 373, |
| "end": 385, |
| "text": "[ MCKEOWN 82", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "FUTURE DIRECTIONS", |
| "sec_num": "9.0" |
| }, |
| { |
| "text": "The TEXT system successfully incorporates principles of relevancy criteria, discourse structure, and focus constraints into a method for generating English text of paragraph length. Previous work on focus of attention has been extended for the task of generation to provide constraints on what to say next. Knowledge about discourse structure has been encoded into schemas that are used to guide the generation process.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CONCLUSION", |
| "sec_num": "10.0" |
| }, |
| { |
| "text": "The use of these two interacting mechanisms constitutes a departure from earlier generation systems. The approach taken in this research is that the generation process should not simply trace the knowledge representation to produce text.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CONCLUSION", |
| "sec_num": "10.0" |
| }, |
| { |
| "text": "Instead, communicative strategies people are familiar with are used to effectively convey information. This means that the same information may be described in different ways on different occasions.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CONCLUSION", |
| "sec_num": "10.0" |
| }, |
| { |
| "text": "The result is a system which constructs and orders a message in response to a given question. Although the system was designed to generate answers to questions about database structure (a feature lacking in most natural language database systems), the same techniques and principles could be used in other application areas (for example, computer assisted instruction systems, expert systems, etc.) where generation of language is needed. ~owl~~ I would like to thank Aravind Joshi, Bonnie Webber, Kathleen McCoy, and Eric Mays for their invaluable comments on the style and content of this paper.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CONCLUSION", |
| "sec_num": "10.0" |
| }, |
| { |
| "text": "Thanks also goes to Kathleen Mccoy and Steven Bossie for their roles in implementing portions of the sys~om.", |
| "cite_spans": [], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CONCLUSION", |
| "sec_num": "10.0" |
| }, |
| { |
| "text": "[MALHOTRA 75]. Malhotra, A. \"Design criteria for a knowledge-based English language system for management: an experimental analysis.\" MAC TR-146, MIT, Cambridge, Mass. (1975) .", |
| "cite_spans": [ |
| { |
| "start": 146, |
| "end": 174, |
| "text": "MIT, Cambridge, Mass. (1975)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CONCLUSION", |
| "sec_num": "10.0" |
| }, |
| { |
| "text": "[ [TENNANT 79]. Tennant, H., \"Experience with the evaluation of natural language question answerers.\" Working paper #18, Univ. of Illinois, Urbana-Champaign, Ill. (1979) .", |
| "cite_spans": [ |
| { |
| "start": 130, |
| "end": 169, |
| "text": "Illinois, Urbana-Champaign, Ill. (1979)", |
| "ref_id": null |
| } |
| ], |
| "ref_spans": [], |
| "eq_spans": [], |
| "section": "CONCLUSION", |
| "sec_num": "10.0" |
| } |
| ], |
| "back_matter": [], |
| "bib_entries": { |
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| "FIGREF0": { |
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| "type_str": "figure" |
| }, |
| "FIGREF2": { |
| "text": "]. Shepherd, H. R., Tne Fine Art of Writinc/, The Macmillan Co., New York, N. Y., 1926. .\" University of Utah, ACM Transactions on Database Systems, Vol. 2, #2, June 1977, pp. 105-33.", |
| "num": null, |
| "uris": null, |
| "type_str": "figure" |
| } |
| } |
| } |
| } |
