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part3- 26. Pragmatics and Computational Linguistics, Materiały naukowe, The Hanbook of Pragmatics

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26. Pragmatics and Computational Linguistics : The Handbook of Pragmatics : Black...
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26. Pragmatics and Computational Linguistics
DANIEL JURAFSKY
Theoretical Linguistics
Ç
Pragmatics
Subject
Key
-Topics
Topics
computational methods and data processing
DOI:
10.1111/b.9780631225485.2005.00028.x
1 Introduction
Introduction
These days there's a computational version of everything. Computational biology, computational
musicology, computational archaeology, and so on, ad infinitum. Even movies are going digital. This
chapter, as you might have guessed by now, thus explores the computational side of pragmatics.
Computational pragmatics might be defined as the computational study of the relation between
utterances and context. Like other kinds of pragmatics, this means that computational pragmatics is
concerned with indexicality, with the relation between utterances and action, with the relation between
utterances and discourse, and with the relationship between utterances and the place, time, and
environmental context of their being uttered.
As Bunt and Black (2000) point out, computational pragmatics, like pragmatics in general, is especially
concerned with INFERENCE. Four core inferential problems in pragmatics have received the most
attention in the computational community: REFERENCE RESOLUTION, the interpretation and generation
of SPEECH ACTS, the interpretation and generation of DISCOURSE STRUCTURE AND COHERENCE
RELATIONS, and ABDUCTION. Each of these four problems can be cast as an inference task, one of
somehow filling in information that isn't actually present in the utterance at hand. Two of these tasks
are addressed in other chapters of this volume; abduction in Hobbs (this volume), and discourse
structure and coherence in Kehler (this volume). Reference resolution is covered in Kehler (2000b). I
have therefore chosen the interpretation of speech acts as the topic of this chapter.
Speech act interpretation, a classic pragmatic problem, is a good choice for this overview chapter for
many reasons. First, the early computational work drew very strongly from the linguistics literature of
the period. This enables us to closely compare the ways that computational linguistic and non-
computational linguistic approaches differ in their methodology. Second, there are two distinct
computational paradigms in speech act interpretation: a logic-based approach and a probabilistic
approach. I see these two approaches as good vehicles for motivating the two dominant paradigms in
computational linguistics: one based on logic, logical inference, feature-structures, and unification,
and the other based on probabilistic approaches. Third, speech act interpretation provides a good
example of pragmatic inference: inferring a kind of linguistic structure which is not directly present in
the input utterance. Finally, speech act interpretation is a problem that applies very naturally both to
written and spoken genres. This allows us to discuss the computational processing of speech input,
and in general talk about the way that computational linguistics has dealt with the differences between
spoken and written inputs.
I like to think of the role of computational models in linguistics as a kind of musical conversation
among three melodic voices. The base melody is the role of computational linguistics as a core of what
we sometimes call Ñmathematical foundationsÒ of linguistics, the study of the formal underpinnings of
models such as rules or trees, features or unification, indices or optimality. The middle line is the
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attempt to do what we sometimes call language engineering. One futuristic goal of this research is the
attempt to build artificial agents that can carry on conversations with humans in order to perform tasks
like answering questions, keeping schedules, or giving directions. The third strain is what is usually
called Ñcomputational psycholinguisticsÒ: the use of computational techniques to build processing
models of human psycholinguistic performance. All of these melodic lines appear in computational
pragmatics, although in this overview chapter we will focus more on the first two roles; linguistic
foundations and language engineering.
The problem with focusing on speech act interpretation, of course, is that we will not be able to
address the breadth of work in computational pragmatics. As suggested above above, the interested
reader should turn to other chapters in this volume (especially Kehler and Hobbs) and also to Jurafsky
and Martin (2000), which covers a number of computational pragmatic issues from a pedagogical
perspective. Indeed, this chapter itself began as an expansion of, and meditation on, the section on
dialogue act interpretation in Jurafsky and Martin (2000).
2 Speech Act Interpretation: the Problem, and a Quick Historical
2 Speech Act Interpretation: the Problem, and a Quick Historical Overview
Overview
The problem of speech act interpretation is to determine, given an utterance, which speech act it
realizes. Of course, some speech acts have surface cues to their form; some questions, for example,
begin with wh-words or with aux-inversion. The Literal Meaning Hypothesis (Gazdar 1981), also called
the Literal Force Hypothesis (Levinson 1983), is a strong version of this hypothesis, suggesting that
every utterance has an illocutionary force which is built into its surface form. According to this
hypothesis, aux-inverted sentences in English have QUESTION force; subject-deleted sentences have
IMPERATIVE force, and so on (see Sadock, this volume).
But it has long been known that many or even most sentences do not seem to have the speech act type
associated with their syntactic form. Consider two kinds of examples of this phenomenon. One
example is INDIRECT REQUESTS, in which what looks on the surface like a question is actually a polite
form of a directive or a request to perform an action. The sentence:
(1) Can you pass the salt?
looks on the surface like a yes-no question asking about the hearer's ability to pass the salt, but
functions actually as a polite directive to pass the salt.
There are other examples where the surface form of an utterance doesn't match its speech act form.
For example, what looks on the surface like a statement can really be a question. A very common kind
of question, called a CHECK question (Labov and Fanshel 1977, Carletta et al. 1997b) is used to ask the
other participant to confirm something that this other participant has privileged knowledge about.
These checks are questions, but they have declarative word order, as in the bold-faced utterance in the
following snippet from a travel agent conversation:
(2) A: I was wanting to make some arrangements for a trip that I'm going to be taking uh to LA
uh beginning of the week after next.
B: OK uh let me pull up your profile and I'll be right with you here. [pause]
B: And you said you wanted to travel next
And you said you wanted to travel next
And you said you wanted to travel next week
week
week?
A: Uh, yes.
There are two computational models of the interpretation of speech acts. The first class of models was
originally motivated by indirect requests of the Ñpass the saltÒ type. Gordon and Lakoff (1971), and
then Searle (1975a), proposed the seeds of this
INFERENTIAL
approach. Their intuition was that a
sentence like Can you pass the salt? is unambiguous, having the literal meaning of a question: Do you
have the ability to pass me the salt? The request speech act Pass me the salt is inferred by the hearer in
a later step of understanding after processing the literal question. Computational implementations of
this idea focus on using belief logics to model this inference chain.
The second class of models has been called CUE-BASED or PROBABILISTIC (Jurafsky and Martin 2000).
The name CUE-BASED draws on the key role of cues in such psychological models as the Competition
Model of Bates and MacWhinney (MacWhinney et al. 1984, MacWhinney 1987). These models are
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motivated more by indirect requests like CHECK questions. Here the problem is to figure out that what
looks on the surface like a statement is really a question. Cue-based models think of the surface form
of the sentence as a set of CUES to the speaker's intentions. Figuring out these intentions does require
inference, but not of the type that chains through literal meanings.
These two models also differ in another important way. The inferential models are based on belief
logics and use logical inference to reason about the speaker's intentions. The cue-based models tend
to be probabilistic machine learning models. They see interpretation as a classification task, and solve
it by training statistical classifiers on labeled examples of speech acts.
Despite their differences, these models have in common the use of a kind of abductive inference. In
each case, the hearer infers something that was not contained directly in the semantics of the input
utterance. That makes them an excellent pair of examples of these two different ways of looking at
computational linguistics. The next section introduces a version of the inferential model called the
PLAN INFERENCE or BDI model, and the following section the CUE-BASED model.
3 The Plan Inference (or BDI) Model of
3 The Plan Inference (or BDI) Model of Speech Act Interpretation
Speech Act Interpretation
The first approach to speech act interpretation we will consider is generally called the BDI (belief,
desire, and intention) or PLAN-BASED model, proposed by Allen, Cohen, and Perrault and their
colleagues (e.g. Allen 1995). Bunt and Black (2000: 15) define this line of inquiry as follows:
to apply the principles of rational agenthood to the modeling of a (computer-based)
dialogue participant, where a rational communicative agent is endowed not only with
certain private knowledge and the logic of belief, but is considered to also assume a
great deal of common knowledge/beliefs with an interlocutor, and to be able to update
beliefs about the interlocutor's intentions and beliefs as a dialogue progresses.
The earliest papers, such as Cohen and Perrault (1979), offered an AI planning model for how speech
acts are generated. One agent, seeking to find out some information, could use standard planning
techniques to come up with the plan of asking the hearer to tell the speaker the information. Perrault
and Allen (1980) and Allen and Perrault (1980) also applied this BDI approach to comprehension
comprehension
comprehension,
specifically the comprehension of indirect speech effects.
Their application of the BDI model to comprehension draws on the plan-inference approach to
dialogue act interpretation, first proposed by Gordon and Lakoff (1971) and Searle (1975a). Gordon,
Lakoff, and Searle noticed that there was a structure to what kind of things a speaker could do to make
an indirect request. In particular, they noticed that a speaker could mention or question various quite
specific properties of the desired activity to make an indirect request. For example, the air travel
request, ÑGive me certain flight informationÒ can be realized as many different kinds of indirect
requests. Here is a partial list from Jurafsky and Martin (2000) with examples from the ATIS
1
corpus of
sentences spoken to a computerized speech understanding system for planning air travel:
1 The speaker can question the hearer's ability to perform the activity:
¤ Can you give me a list of the flights from Atlanta to Boston?
¤ Could you tell me if Delta has a hub in Boston?
¤ Would you be able to, uh, put me on a flight with Delta?
2 The speaker can mention speaker's wish or desire about the activity:
¤ I want to fly from Boston to San Francisco.
¤ I would like to stop somewhere else in between.
¤ I'm looking for one-way flights from Tampa to Saint Louis.
¤ I need that for Tuesday.
¤ I wonder if there are any flights from Boston to Dallas.
3 The speaker can mention the hearer's doing the action:
¤ Would you please repeat that information?
¤ Will you tell me the departure time and arrival time on this American flight?
4 The speaker can question the speaker's having permission to receive results of the action:
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¤ May I get a lunch on flight UA 21 instead of breakfast?
¤ Could I have a listing of flights leaving Boston?
Based on the realization that there were certain systemic ways of making indirect requests, Searle
(1975a: 73) proposed that the hearer's chain of reasoning upon hearing Can you give me a list of the
flights from Atlanta to Boston ? might be something like the following (Searle's sentence was actually
different; I've modified it to this ATIS example):
1 X has asked me a question about whether I have the ability to give a list of flights.
2 I assume that X is being cooperative in the conversation (in the Gricean sense) and that his
utterance therefore has some aim.
3 X knows I have the ability to give such a list, and there is no alternative reason why X should
have a purely theoretical interest in my list-giving ability.
4 Therefore X's utterance probably has some ulterior illocutionary point. What can it be?
5 A preparatory condition for a directive is that the hearer have the ability to perform the
directed action.
6 Therefore X has asked me a question about my preparedness for the action of giving X a list
of flights.
7 Furthermore, X and I are in a conversational situation in which giving lists of flights is a
common and expected activity.
8 Therefore, in the absence of any other plausible illocutionary act, X is probably requesting me
to give him a list of flights.
The inferential approach thus explains why Can you give me a list of flights from Boston? is a
reasonable way of making an indirect request in a way that Boston is in New England is not: the former
mentions a precondition for the desired activity, and there is a reasonable inferential chain from the
precondition to the activity itself.
As we suggested above, Perrault and Allen (1980) and Allen and Perrault (1980) applied this BDI
approach to the comprehension of indirect speech effects, essentially cashing out Searle's (1975a)
promissory note in a computational formalism.
I'll begin by summarizing Perrault and Allen's formal definitions of belief and desire in the predicate
calculus. I'll represent ÑS believes the proposition PÒ as the two-place predicate B(S,P). Reasoning about
belief is done with a number of axiom schemas inspired by Hintikka (1969) (such as B(A,P) Ӽ B(A, Q) ӄ
B (A,P Ӽ Q); see Perrault and Allen 1980 for details). Knowledge is defined as Ñtrue beliefÒ; S knows that
P will be represented as KNOW (S,P), defined as follows:
KNOW(S,P) Ж P Ӽ B(S,P)
In addition to knowing that, we need to define knowing whether. S knows whether (KNOWIF) a
proposition P is true if S KNOWs that P or S KNOWs that ÁP:
KNOWIF(S,P) Ж KNOW(S,P) ӽ KNOW(S, ÁP)
The theory of desire relies on the predicate WANT. If an agent S wants P to be true, we say WANT(S,P),
or W(S,P) for short. P can be a state or the execution of some action. Thus if ACT is the name of an
action, W(S,ACT (H)) means that S wants H to do ACT. The logic of WANT relies on its own set of axiom
schemas just like the logic of belief.
The BDI models also require an axiomatization of actions and planning; the simplest of these is based
on a set of ACTION SCHEMAS similar to the AI planning model STRIPS (Fikes and Nilsson 1971). Each
action schema has a set of parameters with CONSTRAINTS about the type of each variable, and three
parts:
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¤ P
RECONDITIONS
: Conditions that must already be true in order to successfully perform the
action.
¤ E
FFECTS
: Conditions that become true as a result of successfully performing the action.
¤ B
ODY
: A set of partially ordered goal states that must be achieved in performing the action.
In the travel domain, for example, the action of agent A booking flight F for client C might have the
following simplified definition:
BOOK
-FLIGHT(A,C,F)
FLIGHT(A,C,F):
FLIGHT(A,C,F)
Constraints: Agent(A) Ӽ Flight(F) Ӽ Client(C)
Precondition: Know(A,departure-date(F)) Ӽ Know(A,departure-time(F)) Ӽ
É
Know(A,origin-city(F)) Ӽ Know(A,destination-city(F)) Ӽ
É
Know(A,flight-type(F)) Ӽ Has-Seats(F) Ӽ W(C,(Book(A,C,F))) Ӽ È
Effect:
Flight-Booked(A,C,F)
Body:
Make-Reservation(A,F,C)
Cohen and Perrault (1979) and Perrault and Allen (1980) use this kind of action specification for
speech acts. For example, here is Perrault and Allen's definition for three speech acts relevant to
indirect requests. INFORM is the speech act of informing the hearer of some proposition (the
Austin/Searle ASSERTIVE). The definition of INFORM is based on Grice's 1957 idea that a speaker
informs the hearer of something merely by causing the hearer to believe that the speaker wants them
to know something:
INFORM(S,H,P)
INFORM(S,H,P):
Constraints: Speaker(S) Ӽ Hearer(H) Ӽ Proposition(P)
Precondition: Know(S,P) Ӽ W(S,INFORM(S,H,P))
Effect:
Know(H,P)
Body:
B(H,W(S,Know(H,P)))
INFORMIF is the act used to inform the hearer whether a proposition is true or not; like INFORM, the
speaker INFORMIFs the hearer by causing the hearer to believe the speaker wants them to KNOWIF
something:
INFORMIF(S,H,P)
INFORMIF(S,H,P):
Constraints: Speaker(S) Ӽ Hearer(H) Ӽ Proposition(P)
Precondition: KnowIf(S,P) Ӽ W(S, INFORMIF(S,H,P))
Effect:
KnowIf(H,P)
Body:
B(H, W(S, KnowIf(H,P)))
REQUEST is the directive speech act for requesting the hearer to perform some action:
REQUEST(S,H,ACT)
REQUEST(S,H,ACT):
Constraints: Speaker(S) Ӽ Hearer(H) Ӽ ACT(A) Ӽ H is agent of ACT
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