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<Paper uid="C92-1038">
  <Title>A Fast Algorithm for the Generation of Referring Expressions</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> We simplify previous work in the development of algorithms for the generation of referring expre~ sions while at the same time taking account of psycholinguistic findings and transcript data. The result is a straightforward algorithm that is computationally tractable, sensitive to the preferences of human users, and reasonably domain-independent. We provide a specification of the resources a host system must provide in order to make use of the algorithm, and describe an implementation used in the IDAS system. null Introduction In previous work \[Da189,DH91,Rei90a,Rei90b\] we have proposed algorithms for determining the content of referring expressions. Scrutiny of the psycholinguistics literature and transcripts of human dialogues shows that in a number of respects the behaviour of these algorithms does not correspond to what people do. In particular, as compared to these algorithms, human speakers pay far less attention to reducing the length of a referring expression, and far more attention to making sure they use attributes and values that human hearers can easily process; in the terms introduced in \[Da188,Da189\], hearers are more concerned with the principle of sensitivity than with the principle of efficiency. We have designed a new referring expression generation algorithm that is based on the~ observations, and believe that the new algorithm is more practical for real-world natural language generation systems than the algorithms we have previously proposed. In particular, the algorithm is: * fast: its run-time is linear in the number of distractors, and independent of the number of possible modifiers; * sensitive to human preferences: it attempts to use easily perceivable attributes and basic-level \[Ros78\] attribute values; and * Supported by SERC grant GR/F/36750. E-mail ad- dress is E.Reiter@ed. ac.uk. tAiso of the Centre for Cognitive Science at the University of Edinburgh. E-mail address is R. DaleQed. ac .uk.</Paragraph>
    <Section position="1" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Scotland
</SectionTitle>
      <Paragraph position="0"> * domain-independent: the core algorithm should work in any domain, once an appropriate knowledge base and user model has been set up.</Paragraph>
      <Paragraph position="1"> A version of the algorithm has been implemented within the IDAS natural-language generation system \[RML92\], and it is performing satisfactorily.</Paragraph>
      <Paragraph position="2"> The algorithm presented in this paper only generates definite noun phrases that identify an object that is in the current focus of attention. Algorithms and models that can be used to generate pronominal and one-anaphoric referring expressions have been presented elsewhere, e.g., \[Sid81,GJW83,Da189\]. We have recently begun to look at the problem of generating referring expressions for objects that are not in the current focus of attention; this is discussed in the section on Future Work.</Paragraph>
    </Section>
    <Section position="2" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Background
Distinguishing Descriptions
</SectionTitle>
      <Paragraph position="0"> The term 'referring expression' has been used by different people to mean different things. In this paper, we define a referring expression in intentional terms: a noun phrase is considered to be a referring expression if and only if its only communicative purpose is to identify an object to the hearer, in Kronfeld's terminology \[Kro86\], we only use the modal aspect of Donefian's distinction between attributive and referential descriptions \[Don66\]; we consider a noun phrase to be referential if it is intended to identify the object it describes to the hearer, and attributive if it is intended to communicate information about that object to the hearer. This usage is similar to that adopted by Reiter \[Rei90b\] and Dale and Haddock \[DH91\], but differs from the terminology used by Appelt \[App85\], who allowed 'referring expressions' to satisfy any communicative goal that could be stated in the underlying logical framework.</Paragraph>
      <Paragraph position="1"> We here follow Dale and Haddock \[DH91\] in assuming that a referring expression satisfies the referential communicative goal if it is a distinguishing deacription, i.e., if it is an accurate description of the entity being referred to, but not of any other object in the current context set. We define the context set to be the set of entities that the hearer is currently assumed to be attending to; this is similar ACRES DE COLING-92, NAtCrES, 23-28 AO't~q&amp;quot; 1992 2 3 2 PROC. OF COLING-92, NANTES, AUG. 23-28, 1992 to the notion of a discour~ focus space \[GS86\]. We also define the contrast set to be all elements of the context set except the intended referent. The role of tile conlponcnts of a referring expression can then be regarded as 'ruling out' members of the contrast set. For example, if the speaker wished to identify a small black dog in a situation wlmre tile contrast set consisted of a large white dog and a small black cat, she might choose the adjective black in order to rule out the white dog and the heart noun dog in order to rule out the eat; this results in the referring expression the black dog, which matches the intended referent but no other object in the current context.</Paragraph>
      <Paragraph position="2"> The small dog would also be a succ~csful referring expre~-~ion in this context, under the distinguishing description model.</Paragraph>
    </Section>
    <Section position="3" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Unnecessary Modifiers
</SectionTitle>
      <Paragraph position="0"> A referring expression must communicate enough infornlation to be able to uniquely identify the intended referent in the current discourse context (i.e., it must adhere to the principle of adequacy \[Da188,Da189\]). But.this is not the only constraint a good referring expression must obey; it is clear that many referring expressions that meet this constraint are inappropriate because they couvey incorrect and unwanted conversational implieatures \[Gri75,Rei90a\] to a human hearer.</Paragraph>
      <Paragraph position="1"> One source of such false implicatures can he the pre~ ence of redundant or otherwise unnecessary modifiers in a referring expression. For example, consider two possible referring expressions that a speaker might use to request tilat a hearer sit by a talfle: (l) a. Sit by the table.</Paragraph>
      <Paragraph position="2"> h. Sit by the brown wooden table.</Paragraph>
      <Paragraph position="3"> If the context was such that only one table was visible, and this table was brown raid made of wood, utterances (In) and (lb) would both be distinguishing descriptions that unranbiguously identified the intended referent to the hearer; a hearer who heard either utterance would know where he was supposed to sit. However, a hearer who heard utterance (lb) in such a context might make the additional inferenee that it was important to the disc~mrse that the tM)le was brown and made of wood; for, tile hearer might reason, why else would the speaker include information about the table's colour and material that was not necessary for the reference task? This inferenc~ is an example of a conversational implicature caused by a violation of Grice's maxim of Quantity \[Gri75\].</Paragraph>
    </Section>
    <Section position="4" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Inappropriate Modifiers
</SectionTitle>
      <Paragraph position="0"> Unwanted conversational implicatures can alse be caused by the use of overly specific or otherwise unexpected modifiers. One example is ms follows: (2) IL l,ook at the doq.</Paragraph>
      <Paragraph position="1"> b. Look at the pil bull.</Paragraph>
      <Paragraph position="2"> In a context where there is only one dog present, tile hearer would nommlly expect utterance (2a) to be used, since dog is a basic-level class \[Ros78\] for most native speakers of English. Hence the use of utterance (2b) might implicate to the hearer that the speaker thought it was relevant that the animal was a pit bull and not some other kind of dog \[Cru77\], perhaps because the speaker wished to warn the hearer that the animal might be dangerous; if the speaker had no such intention, site should avoid using utterance (2b), despite the fact that it fulfills the referential communicative goal.</Paragraph>
    </Section>
    <Section position="5" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Previous Work
</SectionTitle>
      <Paragraph position="0"> In previous work \[Dalgg,D}191,Rei90a,Rei90b\] we have noted that the presence of extra information in a referring expression can lead the hearer to make false implicaturcs, and therefore concluded that a referring-expression generation system should taake a strong attempt to ensure that generated referring expressions do not include unnecessary information, either as superfluous NP modifiers or as overly-specific head nouns or attribute values. Dale \[DaI88,DaI89,DH91\] }ins suggested doing this by requiring the generation system to produce mtnimal distinguishing descriptions, i.e., distinguishing descriptions that include as few attributes of the intended referent as possible. Keiter \[Keig0a, Rei90b\] ha.s pointed out that this task is in fact NP-Hard, and has proposed instead that referring expressions should obey three rules: No Unnecessary Components: all components of a referring expremion must be necessary to fulfill the referential goal. For example, the small black dog is not acceptable if the black dog is a distinguishing description, since this means small is an unnecessary component.</Paragraph>
      <Paragraph position="1"> Local Brevity: it should not be po~ible to produce a shorter referring expression by replacing a set of existing modifiers by a single new modifier.</Paragraph>
      <Paragraph position="2"> For exanlple, the sleeping female dog should not he treed if the small dog is a distinguishing description, since the two modifiers sleeping and female can bc replaced by the single modifier small.</Paragraph>
      <Paragraph position="3"> Lexical Preference: this is an extension of the ba.sicAcvel preference proposed by Cruse \[Cru77\]; more details are given in \[Reigl\].</Paragraph>
      <Paragraph position="4"> A referring expression that mt.~ts l\[eiter's constraints cart be foumt in polynomial time if the lexical preference relation meets certain conditions \[Rei90a\]; such a referring expression can not, imwever, always be found in linear time.</Paragraph>
    </Section>
    <Section position="6" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Psychological and Transcript Data
Psychological Evidence
</SectionTitle>
      <Paragraph position="0"> Subsequent to performing the above research, we have looked in some detail at the psychological literature on human generation of referring expressions. This research (e.g., \[FO75,Whi76,Son85, A(X'ES DE COLING-92, NANrES, 23-28 AOl'rl' 1992 2 3 3 I'SOC. hi: COLING-92, NANTES, AUG. 23-28, 1992 Pec891; \[Lev89, pages 129-134\] is a useful summary of much of this work) clearly shows that in many cases human speakem do include unnecessary moditiers in referring expressions; this presumably implies that in many cases human hearers do not make implicatures from the presence of unnecessary modifiers.</Paragraph>
      <Paragraph position="1"> For example, if human subjects are shown a picture of a white bird, a black cup, and a white cup, and are asked to identify the white bird, they frequently say the white bird, even though just the bird would have been sufficient in this ease.</Paragraph>
      <Paragraph position="2"> A partial explanation for this use of redundancy may be that human speakers generate referring expressions incrementally \[Pee89\]. An incremental generation algorithm cannot always detect unnecessary modifiers; in the above example, for instance, one could imagine the algorithm choosing the adjective white to rule out the black cup, and then the noun bird in order to rule out the white cup, without then erasing white because the black cup is also ruled out by bird.</Paragraph>
      <Paragraph position="3"> Another explanation of redundancy might involve the speaker's desire to make it easier for the hearer to identify the object; the speaker might believe, for example, that it is easier for the hearer to identify a white bird than a bird, since colour may be more immediately perceptible than shape. 1.</Paragraph>
      <Paragraph position="4"> Both of the above explanations primarily justify adjectives that have some discriminatory power even if they are redundant in this particular context. In the above example, for instance, white possesses some discriminatory power since it rules out the black cup, even though it does happen to be redundant in the expression the white bird. It would be harder for either of the above factors to explain the use of a modifier with no discriminatory power, e.g., the use of white if all objects in the contrast set were white.</Paragraph>
      <Paragraph position="5"> 2qaere is some psychological research (e.g., \[FO75\]) that suggests that human speakers do not use modifiers that have no discriminatory power, but this research is probably not conclusive.</Paragraph>
      <Paragraph position="6"> Thc argument can be made that psychological realism is not the most important constraint for generation algorithms; the goal of such algorithms should be to produce referring expressions that human hearers will understand, rather than referring expressions that human speakers would utter. The fact that human speakers include redundant modifiers in referring expressions does not mean that NL generation systems are also required to include such modifiers; there is nothing in principle wrong with building generation systems that perform more optimizatious of their output than human speakers. On the other hand, if such beyond-human-speaker optimizations 1Another possible explanation is that speakers may in some cases use precompiled 'reference scripts' instead of computing a referring expression from scratch; such referenoe scripts specify a set of attributes that are included as a group in a referring expression, even if some members of the group have no discriminatory power in the current context are computationally expensive and require complex algorithms, they may not be worth performing; they are clearly unnecessary in some sense, after all, since human speakers do not perform them.</Paragraph>
    </Section>
    <Section position="7" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Transcript Analysis
</SectionTitle>
      <Paragraph position="0"> In addition to the l~ychological literature review, we have also examined a transcript of a dialogue between two humans performing an assembly task. ~ We were particularly interested in questions of modifier choice; if a discriminating description can be formed by adding any one of several modifiers to a head noun, which modifier should be used? In particular, null  1. Which attribute should be used? E.g., is it better to generate the small dog, the black dog, or the female dog, if these are discriminating descriptions but jnst the dog is not? 2. Is it preferable to add a modifier or to use a more specific head noun? E.g., is it better to say the small dog or the chihuahua? 3. Should relative or absolute adjectives be used?  E.g., is it better to say the small dog or the one foot high dog? In our analysis, we observed several phenomena which we believe may generalise to other situations involving spoken, face-tooface language: 1. Human speakers prefer to use adjectives that communicate size, shape, or colour in referring expresC/ sions. In tile above examples, for instance, a human speaker would probably prefer the black dog and the small dog over the female dog.</Paragraph>
      <Paragraph position="1"> 2. Human hearers sometimes have trouble determining if an object belongs to a specialized class. In the above example, for instance, the chihuahua should only be used if the speaker is certain the hearer is capable of distinguishing chihuahuas from other types of dogs. If there is any doubt about the heater's ability to do this, adding an explicit modifier (e.g., the small dog) is a better strategy than using a specialized head noun.</Paragraph>
      <Paragraph position="2"> 3. Human speakers seem to prefer to use relative adjectives, and human hearers seem to have less trouble understanding them. However, human-written instructional texts sometimes use absolute adjectives instead of relative ones; this may be a consequence of the fact that writers cannot predict the context their text will be read in, and hence how readers will interpret relative adjectives. In the above example, therefore, a speaker would be expected to use the small dog, but a writer might use the one foot high dog.</Paragraph>
      <Paragraph position="3"> ZThe transcript was made by Phil Agre and John Batali, from a videotape taken by Candy Sidser. We are very grateful to them for allowing us to use it.</Paragraph>
      <Paragraph position="4"> ACTF~ DE COLING-92, NANqT~, 23-28 Aotrr 1992 2 3 4 PROC. OF COLING-92, NANTES, AUG. 23-28, 1992 The Algorithm Based on the above considerations, we have created a new algorithm for generating referring expressions. This algorithm is simpler and faster than the algorithms proposed in \[Dai89,Rei90a\] because it performs much less length-oriented optimization of its outputi we now believe that the level of optimization suggested in \[Da189,ReigOa\] was unnecessary and psycholinguistically implausible. The algorithm has been implemented as part of a larger natural-language generation system, and we are pleased with its performance to date.</Paragraph>
      <Paragraph position="5"> Assumptions about the Knowledge Base Our algorithm is intended to be reasonably domainindependent. We. do, however, make some assumptions about the structure of the host system's underlying knowledge base, and require that certain interface functions be provided.</Paragraph>
      <Paragraph position="6"> in particular, we assume that: * Every entity is characterised in terms of a collection of attributes and their values. An attrii)ute-value pair is what is sometimes thought of as a property; an example is (colour, red).</Paragraph>
      <Paragraph position="7"> Every entity has as one of its attributes some type. This is a special attribute that corresponds to the kinds of properties that are typically realiT, ed by head nouns; an example is (type, dog). * The knowledge base may organize some attribute values in a subsumption taxonomy (e.g., as is done in KI:ONE \[BS85\] and related KR systems). Such a taxonomy might record, for example, that animM subsumes dog, and that red subsumes scarlet. For such taxonomically-organized values, the knowledge-base or an associated user-model should specify which level of the taxonomy is basic-level for the current user.</Paragraph>
      <Paragraph position="8"> We require that the following interface functions be provided: value(object,attribute) returns the value (if any) that an attribute has for a particular object. Value should return the most specific possible value for this attribute, e.g., chihuahua instead of dog, and scarlet instead of red.</Paragraph>
      <Paragraph position="9"> taxonomy-children(value) returns the immediate children of a value in the taxonomy. For example, taxonomy-children(animal) might be the set {dog, cat, horse, ... }.</Paragraph>
      <Paragraph position="10"> basle-level-value(object.attribute) returns the basic~ level value of an attribute of an object. FOr example, basic-level-value(Garfield, type) might be cat. The knowledge-representation system should in principle allow different basic-level classes to be specified for different users \[Ros78,Rei91\].</Paragraph>
      <Paragraph position="11"> user-knows(object, attribute-value-pair) returns true if the user knows or can easily determine (e.g., by direct visual perception) that the attribute-valuc pair applies to the object; false if the user knows or can easily determine that the attribute-value pair does not apply to the object; and unknown otherwise. FOr exmnple, if object x had the attribute-value pair (type, chihuahua), and the user was capable of distinguishing dogs from eats, then userknows(x, (type, dog)) would be true, while userknows(x, (type, cat)) would be false. If the user was not, however, capable of distinguishing different breeds of dogs, and had no prior knowledge of x's breed, then user-knows(x, (type, chihuahua)) and user~knows(x, (type, poodle)) would both return unknown, since the user would not know or be able to easily determine whether x was a chihuahua, poodle, or some other breed of dog.</Paragraph>
      <Paragraph position="12"> Finally, we a~ume that the global variable *p~eferred-attributes* lists the attributes that human speakers and hearers prefer (e.g., type, size, shape, and colour in the ~.,~embly task transcript mentioned above). These attributes should be listed in order of preference, with the most preferable attribute flint. The elements of this list and their order will vary with the domain, a~ld should be determined by empirical inv~tigation.</Paragraph>
      <Paragraph position="13"> Inputs to the Algorithm In order to construct a reference to a particular em tity, tile host system must provide: - a symbol corresponding to the intended referent; and * a list of symbols correspondiug to the members of the contrast set (i.e., the other entities in focus, besides the intended referent).</Paragraph>
      <Paragraph position="14"> The algorithm returus a list of attribute-value pairs that correspond to tim romantic content of the referring expression to be realized. This list can then be converted into an SPL \[K&amp;~9\] term, as is done in the II)AS implementation; it can also be converted into a recoverable semantic structure of the kind used in Daie's EPICOltE system \[Da188,Dai89\].</Paragraph>
      <Paragraph position="15"> The Algorithm In general terms, the algorithm iterates through the attributes in *preferred-attributes*. For each attribute, it checks if specifying a value for it would rule out at least one member of the contrast set that has not already becu ruled out; if so, this attribute is added to the referring ~t, with a value that is known to the User, rules out as many contrast set members as possible, and, subject to these constraints, is as cl(~e as possible to the basic-level value. The process of adding attribut~value pairs continues mttil a referring expression has been formed that rules out every member of the contrast set. There is no backtracking; once an attribute-value pair has been added to the referring expression, it is not removed even if the addition of subsequent attribute-value pairs make it unnecessary. A head noun (i.e., a value for tim type attribute) is always included, even if it Acres DE COLING-92, NANTES, 23-28 AO~n' 1992 2 3 5 PROC. OV COTING-92, NANTES, AUG. 23-28, 1992</Paragraph>
      <Paragraph position="17"/>
      <Paragraph position="19"> has no discriminatory power (in which ease the basic level value is used); other attribute values are only included if, at the time they were under consideration, they had some discriminatory power.</Paragraph>
      <Paragraph position="20"> More precisely, the algorithm is as shown in Figure 1.</Paragraph>
      <Paragraph position="21"> Here, r is the intended referent, C is the contrast set, P is the list of preferred attributes, D is the set of distractom (contrast set members) that have not yet been ruled out, and L is the list of attribute-value pairs returned, a make-referring-expression is the top level function.</Paragraph>
      <Paragraph position="22"> This returns a list of attribute-value pairs that specify a referring expression for the intended refa For simplicity of expo6ition, the algorithm as described here returns failure if it is not pesaible to rule out all the mernbem of the contrast set. A more robust algorithm might attempt to pur~m other strategies here, e.g, generating a referring expression of the form one of the Xs, or modifying the contrast set by adding navigation information (navigation is discussed in the section on Future Work).</Paragraph>
      <Paragraph position="23"> erent. Note that the attributes are tried in the order specified in the *preferred-attributes* list, and that a value for type is always included, even if type has no discriminatory power.</Paragraph>
      <Paragraph position="24"> find-best-value takes an attribute and an initial value; it returns a value for that attribute that is subsumed by the initial value, accurately describes the intended referent (i.e., subsumes the value the intended referent possesses for the attribute), rules out as many distractors as possible, and, subject to these constraints, is as close as possible in the taxonomy to the initial value.</Paragraph>
      <Paragraph position="25"> rules-out takes an attribut~.~value pair and returns the elements of the set of remaining distractom that are ruled out by this attribute-value pair.</Paragraph>
      <Paragraph position="26"> An Example Assume the task is to create a referring expression for Objectl in a context that also includes Object2  and Object3: * Object1: (type, chihuahua), (size, small), (calour, black) * Object2: (type, chihuahua), (size, large), (colour, white) * Object3: (type, siamese-cat), (size, small), (colour, black)  In other words, r = 0bjectl and (7 = {Objest2, Object3}. Assume that P = {type, colour, size, ... }. When make-referring-expression is called in this context, it initializes L to the empty set and D to C, i.e., to {Object2, Object3}. Find-best-value is then caUed with A = type, and initial-value set to the basic-level type of Object1, which, let us assume, is dog.</Paragraph>
      <Paragraph position="27"> Assume user-knows(Object1, (type, dog)) is true, i.e., the user knows or can easily perceive that Objectl is a dog. Find-best-value then sets value to dog, and examines the taxonomic descendants of dog to see if any of them are accurate descriptions of Object1 (this is the subsumption test) and rule out more distractors than dog does. In this case, the only accurate child of dog is chihuahua, but (type, chihuahua) does not have more discriminatory power than (type, dog) (both rule out {Object3}), so find-best-value returns dog as the best value for the type attribute. Make-referring-expression then verifies that (type. dog) rules out at least one distraetor, and therefore adds this attribute-value pair to L, while removing rules-out((type, dog)) = {Object3} from D. This means that the only remaining distraetor in D is Object2. Make-referring-expression (after cheeking that D is not empty) calls find-best-value again with A = colour (the second member of P). Find-best-value returns Objectl~s basic-level colour value, which is black, since no more specific colour term has more discriminatory power. Make-referring-expression then adds (colour, black) to L and removes rules-out((colour, black)) = {Object2} from D. D is then empty, so the generation task is completed, ACTES DE COLING-92, NANTES, 23-28 AO~r 1992 2 3 6 PROC. OF COLING-92, NArCrEs. AUG. 23-28. 1992 and make-referring-expression returns {(type, dog), (celour, black)} , i.e., a specification for the referring expression the black day. Note that if P had been {type, size, colour, ... } instead of {type, cnlour, size, ...}, make-referring-expeession would have rc~ turned {(type, dog), (size, small)} instead, i.e., the sraall do#.</Paragraph>
    </Section>
    <Section position="8" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Implementation
</SectionTitle>
      <Paragraph position="0"> The algorithm is currently being used within the n)AS system \[RML92\]. ll)hS is a natural lm~guage generation system that generates on-line documentation and help texts from a domain arid linguistic knowledge base, lining user expertise models, user task models, and discourse models.</Paragraph>
      <Paragraph position="1"> IDAS uses a KL-ONE type knowledge repr~entation system, with roles corresponding to attributes and lillem to values. The type attribute is implicit in the position of an object in the taxonomy, and is not explicitly represented. The value and taxonomy-children functions are defined in terms of standard knowledge-base access functions.</Paragraph>
      <Paragraph position="2"> A knowledg~base author can specify explicit basic-level attribute values in IDAS user models, but IDAS is also capable of using heuristics to guess which value is basic-level. The heuristics are fairly simple (e.g., &amp;quot;nse the most general value that is not in the uppermodel \[BKMW90\] and has a one-word realization&amp;quot;), but they seem (so far) to be at least somewhat effective. A *preferred-attributes* list has been crcated for IOAS's domain (complex electronic machinery) by visual inspection of the equipment being documented; its first members are type, colour, and label. The user-knows function simply returns true if the attributc~value pair is accurate and false otherwise; this essentially assumes that the user can visually perceive the value of any attribute in *preferredattributes*, which may not tie true in general.</Paragraph>
      <Paragraph position="3"> The referring expression generation model seems reasonably successful in IDAS. In parLieular, the algorithm lure proven to be useful because: 1. It is fast. The algorithm runs in linear time in the number of distractors, which is probably impossible for any algorithm that includes an explicit brevity requirement (e.g., the algorithms of \[Da189,Rei90a\]). Of equal importance, its run-time is independent of the number of potential attributes that could be used in the referring expreszion. This ks a consequence of the fact that the algorithm does not attempt to find the attribute with the highest discriminatory power, but rather simply takes attributes from the *preferred-attributes* list until it has built a successful referring expression.</Paragraph>
      <Paragraph position="4"> 2. It allows human preferences and capabilities to be taken into consideration. The *preferred-attributes* list, the preference for basic-level values, and the user~knows function are all ways of biasing the algorithm towards generating referring expressions that use attributes and values that hutaan hearers, with all their perceptual limitations, lind easy to process.</Paragraph>
      <Paragraph position="5"> Almost all referring expressions generated by IDAS contain a head noun and zero, one, or perhaps at most two modifiers; longer referring expressions are rare. The most important task of the algorithm is therefore to quickly generate easy-to-understand referring expre~mions in such simple cases; optimal handling of more complex referring expressions is lees important, although the algorithm should be robuat enough to generate something plausible if a long referring expression is needed.</Paragraph>
    </Section>
    <Section position="9" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Future Work
Navigation
</SectionTitle>
      <Paragraph position="0"> As mentioned in the introduction, the algorithm presented here assumes that the intended referent is in the context set. An important question we need to address is what action should be taken if this is not the c~.se, i.e., if the intended referent is not in the current focus of attention.</Paragraph>
      <Paragraph position="1"> Unfortunately, we have very little data available ou which to bose a model of the generation of such referring expressions. Psyclmlinguistic researchers seem to have paid relatively little attention to such eases, and the transcripts we have (to date) examined have contained relatively few instances where the intended referent was not already salient.</Paragraph>
      <Paragraph position="2"> ltowever, we take the view that, in the general case, a referring expression contains two kinds of information: navigation and discrimination. Each de~ scriptor used in a referring expression plays one of these two roles.</Paragraph>
      <Paragraph position="3"> * Navigational, or attention-directing information, is intended to bring the intended referent into the hearer's focus of attention.</Paragraph>
      <Paragraph position="4"> * Discrimination information is intended to distinguish the intended referent from other objects in the hearer's focus of attention; such information has been the subject of this paper.</Paragraph>
      <Paragraph position="5"> Navigational information is not needed if the intended referent is already in the focus of attention. If it is needed, it frequently (although not always) takes the form of loeational information. The IDAS system, for example, can generate referring expressions such as tl~e black power supply in the equipment rack. In this case, in the equipment rack is navigation information that is intended to bring the equipment rack and its components into the hearer's focus of attention, while black power supply is discrimination information that is intended Ix) distinguish the intended referent from other members of the context ~t (e.g., the white power supply that is also present in the equipment rack).</Paragraph>
      <Paragraph position="6"> The navigation model currently implemented in If)AS is simplistic and not theoretically well-justified. We hope to do further research on building a betterju~stified model of navigation.</Paragraph>
      <Paragraph position="7"> AcrEs DE COLING-92, NAbrlns. 23-28 ^o~r 1992 2 3 7 PRoc, OF COLING-92, NANTES, AtJcl. 23-28, 1992</Paragraph>
    </Section>
    <Section position="10" start_page="0" end_page="0" type="sub_section">
      <SectionTitle>
Relative Attribute Values
</SectionTitle>
      <Paragraph position="0"> As mentioned previously, the transcript analysis shows that human speakers and hearers often prefer relative instead of absolute attribute values, e.g., small instead of one inch. Knowledge bases sometimes explicitly encode relative attribute values (e.g., (size. small)), but this can cause difficulties when referring expressions need to he generated in different contexts; a one-inch screw, for example, might be considered to be small in a context where the other screws were all two-inch screws, but large in a context where the other screws were all half-inch screws.</Paragraph>
      <Paragraph position="1"> A better solution is for the knowledge base to record absolute attribute values, and then for the generation algorithm to automatically convert absolute values to relative values, depending on the values that other members of the context set pussc~ for this attribute. Thus, the knowledge base might record that a particular screw had (size. one-inch), and the generation system would choose to call this screw small or/arye depending on the size of the other screws in the context set. We hope to do further research on determining how exactly this process should work.</Paragraph>
      <Paragraph position="2"> Conclusions We have presented an algorithm for the generation of referring expressions that is substantially simpler and faster than the algorithms we have proposed in previous work \[Da189,Rei90a\], largely because it performs much less length-oriented optimization of its output. We have been guided in this simplification effort by psycholinguistic findings and transcript analyses, and believe that the resulting algorithm is a more practical one for natural language generation systems than the ones we proposed previously.</Paragraph>
      <Paragraph position="3"> ACIds DE COLING-92, NANI~S, 23-28 AOUT 1992 2 3 8 PROC. OF COL1NG-92, NANI'ES, AUG. 23-28, 1992</Paragraph>
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