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<Paper uid="C04-1181">
  <Title>Interpreting Vague Utterances in Context</Title>
  <Section position="3" start_page="0" end_page="0" type="metho">
    <SectionTitle>
2 Vague standards in context
</SectionTitle>
    <Paragraph position="0"> We adopt a view of vague predicates motivated by linguistic theory, particularly Kennedy's approach (1999; 2003). We assume that gradable adjectives are associated with measurement functions mapping individuals to degrees on a scale.InFIGLET's drawing domain, the relevant measurements pertain to spatial properties. For long, for example, the measurement maps individuals to their spatial lengths; for small, it maps individuals to degrees on an inverted scale of size.</Paragraph>
    <Paragraph position="1"> Positive gradable adjectives compare measured degrees against a standard on the scale which is derived from context. For example, long says that an object's length exceeds the threshold set by the current standard for length. Other forms, such as comparative adjectives or adjectives with explicit measure phrases, compare degrees differently.</Paragraph>
    <Paragraph position="2"> Importantly, grammar says nothing about how standards for positive gradable adjectives are derived. In other words, contra Cresswell (1977) and others, the interpretation of adjectives is not computed relative to a grammatically-specified &amp;quot;comparison class&amp;quot; of related objects. And, contra Oates et al. (2000) and Roy and Pentland (2002), the interpretation of adjectives need not require statistical knowledge about how objects with different measurements on a scale tend to be described. Instead, standards are derived directly from an evolving context by the general principles that govern pragmatic resolution of context dependence.</Paragraph>
    <Paragraph position="3"> Kennedy synthesizes a range of evidence for this claim. Here we go further, and provide a formal, implemented model. We can sketch the evidence and our model by considering two key examples.</Paragraph>
    <Paragraph position="4"> First, we illustrate that vagueness depends directly on specific contextually-relevant distinctions. Consider the session with FIGLET shown in Figure 2. The user has elected to draw two objects sideby-side. The initial context just contains a square. The user utters (2).</Paragraph>
    <Paragraph position="5"> (2) Make a small circle.</Paragraph>
    <Paragraph position="6"> To interpret (2) it doesn't seem to help to appeal to general knowledge about how big circles tend to be.</Paragraph>
    <Paragraph position="7"> (It's quite convoluted to even frame the idea in a sensible way.) Graff (2000) observes that standards often implicitly appeal to what we expect about particular individuals, not just what we know about similar individuals. In context, here, the user just seems to be asking for a circle vaguely smaller than the square. This is the interpretation FIGLET builds; to comply, FIGLET draws the circle an arbitrary but representative possible size. The point is that salient objects and actions inevitably set up meaningful dis-Initial figure state. After the user utters (2). Initial figure state. After the user utters (3).  in (3): Make the small square a circle.</Paragraph>
    <Paragraph position="8"> tinctions in the context. Interlocutors exploit these distinctions in using vague adjectives.</Paragraph>
    <Paragraph position="9"> Figure 3 illustrates that understanding vagueness is part of a general problem of understanding utterances. Figure 3 shows FIGLET's action in a more complex context, containing two squares of different sizes. We consider the user's instruction (3): (3) Make the small square a circle.</Paragraph>
    <Paragraph position="10"> FIGLET's action changes the smaller of the two squares. The standard behind this interpretation is implicitly set to differentiate the contextuallysalient objects from one another; the natural resolution of (3) does not require that either square be definitely small (Kyburg and Morreau, 2000). In Figure 3, for example, there are different potential standards that would admit either both squares or neither square as small. However, we can rule out these candidate standards in interpreting (3). The user's communicative intention must explain how a unique square from the context can be identified from (3) using a presupposed small standard. If that standard is too big, both squares will fit. If that standard is too small, neither square will fit. Only when that standard falls between the sizes of the squares does (3) identify a unique square.</Paragraph>
    <Paragraph position="11"> The examples in Figures 2 and 3 show two ways new standards can be established. Once established, however, standards become part of the evolving context (Barker, 2002). Old standards serve as defaults in interpreting subsequent utterances. Only if no better interpretation is found will FIGLET go back and reconsider its standard. This too is general pragmatic reasoning (Stone and Thomason, 2003).</Paragraph>
    <Paragraph position="12"> 3 Dimensions of context in interpretation To cash out our account of contextual reasoning with vagueness, we need to characterize the context for practical dialogue. Our account presupposes a context comprising domain and situation knowledge, task context and linguistic context. In this section, we survey each of these dimensions of context, and show how they converge in the resolution of underspecification across a wide range utterances.</Paragraph>
    <Paragraph position="13"> Domain and situation knowledge describes the commonsense structure of the real-world objects and actions under discussion. Practical dialogue restricts this otherwise open-ended specification to the circumscribed facts that are directly relevant to an ongoing collaboration. For example, in our drawing domain, individuals are categorized by a few types: types of shape such as circles and squares; and types of depiction such as eyes and heads. These types come with corresponding constraints on individuals.</Paragraph>
    <Paragraph position="14"> For example, the shape of a mouth may be a line, an ellipse, or a rectangle, while the shape of a head can only be an ellipse. These constraints contribute to interpretation. For instance, a head can never be described as a line, for example, since heads cannot have this shape.</Paragraph>
    <Paragraph position="15"> Task context tracks collaborators' evolving commitment to shared goals and plans during joint activity. In FIGLET's drawing domain, available actions allow users to build figure parts by introducing shapes and revising them. Our experience is that users' domain plans organize these actions hierarchically into strategic patterns. For example, users tend to complete the structures they begin drawing before drawing elsewhere; and once they are satisfied with what they have, they proceed in natural sequence to a new part nearby. Task context plays a powerful role in finding natural utterance interpretations. By recording a plan representation and keeping track of progress in carrying it out, FIGLET has access to a set of candidate next actions at each point in an interaction. Matching the user's utterance against this candidate set restricts the interpretation of instructions based on the drawing already created and the user's focus of attention within it.</Paragraph>
    <Paragraph position="16"> For example, if the user has just drawn the right eye onto an empty face, they are likely to turn to the left eye next. This context suggests making a winking left eye in response to draw a line, an interpretation that might not otherwise be salient.</Paragraph>
    <Paragraph position="17"> Linguistic context records the evolving status of pragmatic distinctions triggered by grammatical conventions. One role of the linguistic context is its contribution to distinguishing the prominent entities Initial figure state. After the user utters (4):  that can serve as the referents of pronouns and other reduced expressions. To see this, note that, as far as domain knowledge and task context go, the instruction make it bigger could apply to any object currently being created. If the figure is hierarchical, there will be many possibilities. Yet we typically understand it to refer specifically to an object mentioned saliently in the previous utterance. The linguistic context helps disambiguate it.</Paragraph>
    <Paragraph position="18"> Figure 4 illustrates how the three different dimensions of context work together. It illustrates an interaction with FIGLET where the user has just issued an instruction to create two eyes, resulting in the figure state shown at the left in Figure 4. The user's next instruction is (4):  (4) Draw a line underneath.</Paragraph>
    <Paragraph position="19">  We focus on how the context constrains the position and orientation of the line.</Paragraph>
    <Paragraph position="20"> Linguistic context indicates that underneath should be understood as underneath the eyes.This provides one constraint on the placement of the line.</Paragraph>
    <Paragraph position="21"> Task context makes drawing the mouth a plausible candidate next action. Domain knowledge shows that the mouth can be a line, but only if further constraints on position, orientation and length are met. In understanding the instruction, FIGLET applies all these contextual constraints simultaneously. The set of consistent solutions--drawing a horizontal line at a range of plausible mouth positions below the eyes--constitutes the utterance interpretation. FIGLET acts to create the result in Figure 4 by choosing a representative action from this set.</Paragraph>
  </Section>
  <Section position="4" start_page="0" end_page="2" type="metho">
    <SectionTitle>
4 Interpreting vague utterances in context
</SectionTitle>
    <Paragraph position="0"> In our approach, the linguistic context stores agreed standards for vague predicates. Candidate standards are determined using information available from domain knowledge and the current task context. In FIGLET's drawing domain, possibilities include the actual measurements of objects that have already been drawn. They also include the default domain measurements for new objects that task context says could be added. Setting standards by a measurement is our shorthand for adopting an implicit range of compatible standards; these standards remain vague, especially since many options are normally available (Graff, 2000).</Paragraph>
    <Paragraph position="1"> We treat the use of new candidate standards in interpretation as a case of presupposition accommodation (Bos, 2003). In presupposition accommodation, the interpretation of an utterance must be constructed using a context that differs from the actual context. When speakers use an utterance which requires accommodation, they typically expect that interlocutors will update the dialogue context to include the additional presumptions the utterance requires. We assume that all accommodation is sub-ject to two Gricean constraints. First, we assume whenever possible that an utterance should have a uniquely identifiable intended interpretation in the context in which it is to be interpreted. Second, we assume that when interpretations in alternative contexts are available, the speaker is committed to the strongest one--compare Dalrymple et al. (1998).</Paragraph>
    <Paragraph position="2"> Inferring standards for vague predicates is a special case of this general Gricean reasoning.</Paragraph>
    <Paragraph position="3"> The principles articulated thus far in Sections 2-4 allow us to offer a precise explanation of FIGLET's behavior as depicted in Figure 1. The user starts drawing a face with an empty figure. In this domain and task context, make two circles fits a number of possible actions. For example, it fits the action of drawing a round head and its gaping mouth. However, in (1a), what the user actually says is make two small circles. The interpretation for (1a) must accommodate a standard for small and select from the continuum of size possibilities two new circles that meet this standard.</Paragraph>
    <Paragraph position="4"> The standards in this context are associated with the size distinctions among potential new objects.</Paragraph>
    <Paragraph position="5"> The different qualitative behavior of these standards in interpretation can be illustrated by the standards set from possible new circular objects that are consistent with the face-drawing task. We can set the standard from the default size of an eye, from the default size of a mouth (larger), or from the default size of a head (larger still).</Paragraph>
    <Paragraph position="7"> dard allows all smaller objects to be created next, these standards lead to 1, 3, and 6 interpretations, respectively. So we recover the standard from the eye, which results in a unique interpretation.</Paragraph>
    <Paragraph position="8">  Note that there are many potential sources of standards for small that FIGLET does not currently pursue. E.g. the average size of all objects already in the figure. We believe that general In tandem with its response, FIGLET tracks the changes to the context. The task context is updated to note that the user has drawn the eyes and must continue with the process of creating and revising the features of the face. The linguistic context is updated to include the new small standard, and to place the eyes in focus.</Paragraph>
    <Paragraph position="9"> This updated context provides the background for (1b), the user's next instruction draw a long line underneath. In this context, as we saw with Figure 4, context makes it clear that any response to draw a line underneath must draw the mouth.</Paragraph>
    <Paragraph position="10"> Thus, unlike in (1a), all the interpretations here have the same qualitative form. Nevertheless, FIGLET's Gricean reasoning can still adjust the standard for length to differentiate interpretations quantitatively, and thereby motivate the user's use of the word long in the instruction. FIGLET bases its possible standards for length on both actual and potential objects. It can set the standard from an actual eye or from the two eyes together; and it can set the standard from the default mouth or head. The mouth, of course, must fit inside the head; the largest standard is ruled out. All the other standards lead to unique interpretations. Since the length of the two eyes together is the strictest of the remaining standards, it is adopted. This interpretation leads FIGLET to the response illustrated at the right in Figure 1.</Paragraph>
  </Section>
  <Section position="5" start_page="2" end_page="2" type="metho">
    <SectionTitle>
5 Implementation
</SectionTitle>
    <Paragraph position="0"> We have implemented FIGLET in Prolog using CLP(R) real constraints (Jaffar and Lassez, 1987) for metric and spatial reasoning. This section presents a necessarily brief overview of this implementation; we highlight how FIGLET is able to exactly implement the semantic representations and pragmatic reasoning presented in Sections 2-4. We offer a detailed description of our system and discuss some of the challenges of building it in DeVault and Stone (2003).</Paragraph>
    <Section position="1" start_page="2" end_page="2" type="sub_section">
      <SectionTitle>
5.1 Semantic representation
</SectionTitle>
      <Paragraph position="0"> In FIGLET, we record the semantics of user instructions using constraints, or logical conjunctions of open atomic formulas, to represent the contextual requirements that utterances impose; we view these constraints as presuppositions that speakers make in using the utterance. We assume matches take the form of instances that supply particular domain representations as suitable values for variables. Stone (2003) motivates this framework in detail.</Paragraph>
      <Paragraph position="1"> methods for specifying domain knowledge will help provide the meaningful task distinctions that serve as candidate standards for vague predicates on our approach, but pursuing this hypothesis is beyond the scope of this paper.</Paragraph>
      <Paragraph position="2"> In (5a-d), we show the presuppositions FIGLET assigns to an utterance of Make two small circles, arranged to show the contributions of each individual word. In (5e), we show the contribution made by the utterance to an evolving dialogue; the effect is to propose that an action be carried out.</Paragraph>
      <Paragraph position="3">  (5) a. simple(A)^target(A;X)^fits plan(A)^</Paragraph>
      <Paragraph position="5"> We formulate these constraints in an expressive ontology. We have terms and variables for actions,suchasA;forsituations,suchasnow and result(A;now);forobjects,suchasX; for standards for gradable vague predicates (scale-threshold pairs), such as S; and for quantitative points and intervals of varying dimensionality, as necessary.</Paragraph>
    </Section>
    <Section position="2" start_page="2" end_page="2" type="sub_section">
      <SectionTitle>
5.2 Pragmatic reasoning
</SectionTitle>
      <Paragraph position="0"> Constraint networks such as (5a-e) provide a uniform venue for describing the various contextual dependencies required to arrive at natural utterance interpretations. Thus, the contextual representation and reasoning outlined in Sections 3 and 4 is realized by a uniform mechanism in FIGLET: specifications of how to reason from context to find solutions to these constraints.</Paragraph>
      <Paragraph position="1"> For example, Section 3 described domain knowledge that links particular object types like eyes and heads with type-specific constraints. In our implementation, we specify real and finite constraints that individuals of each type must satisfy. In order for an individual e of type t to serve as part of a solution to a constraint network like (5a-e), e must additionally meet the constraints associated with type t.In this way, FIGLET requires utterance interpretations to respect domain knowledge.</Paragraph>
      <Paragraph position="2"> Solving many of the constraints appearing in (5ae) requires contextual reasoning about domain actions and their consequences. Some constraints characterize actions directly; thus simple(A) means that A is a natural domain action rather than an abstruse one. Constraints can describe the effects of actions by reference to the state of the visual display in hypothetical situations; thus holds(result(A;now);shape(X;circle)) means that the individual X has a circular shape once action A is carried out. Constraints can additionally characterize causal relationships in the domain; thus target(A;X) means that action A directly affects X, and the constraints of (5a-d) together mean that carrying out action A in the current situation causes two small circles to become visible. These constraints are proved in FIGLET by what is in effect a planner that can find complex actions that achieve specified effects via a repertoire of basic domain actions.</Paragraph>
      <Paragraph position="3"> Task context is brought to bear on interpretation through the fits plan(A) constraint of (5a). FIGLET uses a standard hierarchical, partially ordered plan representation to record the structure of a user's task. We specify the solutions to fits plan(A) to be just those actions A that are possible next steps given the user's current state in achieving the task.</Paragraph>
      <Paragraph position="4"> Since these task-appropriate actions can factor additional constraints into interpretation, enforcing the fits plan(A) constraint can help FIGLET identify a natural interpretation.</Paragraph>
      <Paragraph position="5"> As discussed in Section 4, FIGLET records a list of current standards for vague scalar adjectives in the linguistic context. The constraint standard(small;S) of (5c) connects the overall utterance interpretation to the available standards for small in the linguistic context. FIGLET interprets utterances carrying semantic constraints of the form standard(vague-predicate;S) in one or two stages.</Paragraph>
      <Paragraph position="6"> In the first stage, the constraint is solved just in case S is the prevailing standard for vague-predicate in the linguistic context. If there is no prevailing standard for an evoked vague property, or if this stage does not yield a unique utterance interpretation, then FIGLET moves to a second stage in which the constraint is solved for any standard that captures a relevant distinction for vague-predicate in the context. If there is a strongest standard that results in a unique interpretation, it is adopted and integrated into the new linguistic context.</Paragraph>
    </Section>
    <Section position="3" start_page="2" end_page="2" type="sub_section">
      <SectionTitle>
5.3 Parsing and Interpretation
</SectionTitle>
      <Paragraph position="0"> Language understanding in FIGLET is mediated by a bottom-up chart parser written in Prolog. As usual, chart edges indicate the presence of recognized partial constituents within the input sequence. In addition, edges now carry constraint networks that specify the contextual reasoning required for understanding. In addition to finite instances (Schuler, 2001), these networks include real constraints that formalize metric and spatial relationships. Interpretation of these networks is carried out incrementally, during parsing; each edge thus records a set of associated candidate interpretations. Since domain reasoning can be somewhat time-intensive in our current implementation, we adopt a strategy of delaying the solution of certain constraints until enough lexical material has accrued that the associated problem-solving is judged tractable (DeVault and Stone, 2003).</Paragraph>
    </Section>
  </Section>
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