\documentstyle[aaai]{article} 
\begin{document}
\title{Goals for Future Computational Models \\
of Conversational Implicature}

\author{Nancy Green and Jill Fain Lehman\thanks{This 
research was supported in part by the Wright Laboratory,
Aeronautical Systems Center, Air Force Materiel Command, USAF, and the
Advanced Research Projects Agency under grant number F33615-93-1-1330.
The views and conclusions contained in this document are those of the
authors and should not be interpreted as necessarily representing the
official policies or endorsements, either expressed or implied, of
Wright Laboratory or the U.S.~government.}
 \\
  Computer Science Department \\
  Carnegie Mellon University \\
  Pittsburgh, PA 15231-3891, USA \\ 
  (Internet: nancy.green@cs.cmu.edu)}
\maketitle
\begin{abstract}
%\section*{Abstract}
\begin{quote}
We propose research goals for future models of conversational implicature,
and describe how NL-Soar, a computational model of dialogue 
interpretation and generation implemented within the Soar cognitive 
architecture, serves as a step towards achieving these goals in a unified
framework.
\end{quote}
\end{abstract}

One avenue of research on conversational implicature \cite{Grice75}
in non-figurative discourse which should be
addressed in the immediate future is the construction of computational
models of agents who
generate and comprehend conversational implicatures
in the course of real-time dialogue.
In the first section of this paper, 
we propose a set of research goals for such models.
In the second section, we describe an earlier plan-based approach to
conversational implicature which motivates our current research.
Finally, we describe how our
current work in NL-Soar, a computational model of dialogue
interpretation and generation implemented within the Soar cognitive 
architecture, serves as a step towards achieving these goals in a unified
framework.

\section{Goals for Future Models}

We believe that future computational models of conversational
implicature (CI) should aspire to meet the following goals:
\begin{itemize}
\item To derive all and only the conversationally licensed
      inferences which
      a speaker intends for the hearer to recognize as intended in
      the current discourse context.
      To model the contextual dependence of CI.
      To model cancelability phenomena.
      To be capable of handling
      multiple types of CI without unwarranted conflict.
\item To provide tractable procedures for generating and interpreting CI.
      To model a speaker's motivation for use (or avoidance) of
      discourse conveying CI, and to model the processes of selecting 
      {\em what} to say and {\em how} to say it.  
      To model a hearer's process of
      recognizing that the speaker must have meant ``more than what he said'' 
      and to model the process by which CI are derived.
\item To be a cognitively plausible model.  That is, to be subject
      to real-time performance constraints and human resource limitations.
\item To learn from experience how to interpret novel CI.
      That is, new classes of CI can be interpreted without requiring
      the model's builder to provide new licensing rules specific to 
      those classes.  
\item To model diachronically how certain CI become semi-conventional.
      E.g., to model the association of particular linguistic forms with 
      certain CI,
      making it problematic to use those forms to convey just their
      original meaning.
\item To lead to new predictions about and insights into use of language
      and how it interacts with other
      cognitive capabilities (e.g., planning and reasoning) of embodied
      agents capable of interacting with their environment through
      linguistic and non-linguistic means.
\end{itemize}

To elaborate, the first of the above goals suggests what we mean by an
adequate model of conversational implicature as opposed to other
aspects of discourse interpretation, e.g., entailment, presupposition, and
ambiguity.  Although it has been notoriously difficult to formulate a
precise definition of CI, the notions of speaker meaning \cite{Grice57},
mutual belief \cite{clark81},
and speaker plans have been shown to be important 
\cite{Hirschberg85,McCafferty87,thomason90}.
In addition to accounting for cancellation
by previous and subsequent context, models of CI should account for the
change in a potential conversational implicature's status
from cancelable to noncancelable as a discourse progresses\cite{Gunji}.  
Moreover, this ``window'' of cancelability is determined by a 
discourse's intentional structure \cite{Green94}. 

The second goal describes what we mean by a computational model of CI.
The goal of cognitive plausibility can be justified not only on scientific
but also on practical grounds.
First, since human conversationalists comprehend and generate CI,
searching for a cognitively plausible model is a useful research strategy.
Second, if the model is to be used in human-computer interaction,
it is desirable for it to meet the same real-time performance requirements
as its human conversational partners.  

The goals of learning to interpret novel CI and of
modeling how certain CI become semi-conventional
are desirable since it is unclear
whether human model builders can anticipate all the situations in which
CI may arise. 
Finally, the last goal seems desirable, not only for the sake of science,
but also to enable our models to better approximate human
competence in the long term.  For example, for a first approximation,
a model of a certain type of CI might be
built which presupposes that the agent has acquired knowledge of principles
of politeness.  A more powerful model is one that explains
how such principles arise, e.g., by learning through social
interaction with other agents.

Traditionally, most work on CI has focused on the first two of the above goals.
In contrast, our research strategy is to begin by building a foundational
model of dialogue processing which will enable all of the above goals 
to be addressed in a unified framework.  In the next section, we provide
some motivation for this strategy.

\section{A Plan-based Approach to Indirect Answers}

As for the goal of interpreting novel types of CI without requiring the 
model builder to provide licensing rules specific to that class of CI,
first, let us consider the first author's computational model for 
interpreting and generating indirect answers to Yes-No questions 
\cite{Green94}.  By use of an indirect answer as in (1), R conversationally
implicates the intended answer ({\em no}) to Q's question.  
\begin{tabbing}
\=(1)...\=Q:..\=xxx \kill 
\>(1)   \>Q:  \>Are you going to rent a car in Chicago? \\
\>      \>R:  \>I can't drive.\\
\end{tabbing}
Interpretation
and generation of indirect answers are modeled, respectively, as
recognition and construction of the responder's discourse plan specification
for a full answer.  A full answer consists of the requested information,
i.e. the direct answer, and possibly extra relevant information.
A discourse plan specification relates a speaker's beliefs and discourse
goals to his program of communicative actions.  An indirect answer is
the result of the responder providing only part of the planned response,
but expecting that his full discourse plan specification will be recognized
by the hearer.  

An important knowledge source in that model is a shared set
of discourse plan operators encoding generic programs for conveying full
answers.  A wide range of full answer types are characterized
by the operators, which encode generalizations about {\em subject-matter}
coherence relations \cite{mannthompson87} in responses, as well as about
general discourse strategies motivating the selection of the ``extra'' 
information in responses.  These operators play a key role in recognizing
and constructing the speaker's discourse plan specification.  Note that
the model does not employ a licensing rule specific to indirect answers.
Instead, an indirect answer is comprehended as a result of attempting to
recognize the responder's discourse plan specification for a full answer.
(When a full answer is not supplied by a speaker, 
the model uses a combination of 
hypothesis generation and plan inference to recognize his discourse plan
specification.)

The discourse plan operators used in that model were constructed by the 
model's builder
based on her analysis of examples of uses of indirect answers.
An interesting question arises as to whether such a discourse-plan-based
approach has any cognitive plausibility.
Human conversationalists seem to be able to comprehend many CI in real-time,
suggesting an approach that does not require reasoning from first principles
in all cases, i.e. an approach that uses some type of ``compiled''
knowledge.  As a model of natural language processing situated within a
cognitive architecture, NL-Soar provides us with a starting point for 
investigating this and related questions, e.g., if people do use discourse 
operators, how could they learn them?, 
what sorts of discourse operators are possible?, and could the same discourse
operators be used in interpretation as well as in generation?
Thus, we are currently investigating how NL-Soar's learning mechanism
can be used to acquire discourse operators.

\section{Dialogue Modeling in NL-Soar}

NL-Soar is a computational model of real-time natural language generation
and comprehension implemented within the Soar cognitive architecture.
Soar is a candidate unified theory of human cognition which has been
implemented as a computational system for modeling cognitive 
tasks.\footnote{For more information about Soar, see \cite{Newell,Gentle-Soar} 
or {\em http://www.isi.edu/soar/soar.html} (the Soar home page).}
NL-Soar has been implemented using the mechanisms provided by Soar, e.g.,
short-term and long-term memory, control structure, and basic learning
mechanism.  Our current research in NL-Soar is directed towards building
a dialogue system which can participate in conversation 
in real-time within a task 
domain.\footnote{Previous versions of NL-Soar addressed real-time
parsing \cite{Lewis93} and sentence generation \cite{Rubinoff93}.
Other areas of current research include using NL-Soar in modeling
second language acquisition and simultaneous translation.}

In Soar, cognitive tasks are performed in a problem space
by applying operators to states to create new states.  
The knowledge defining an operator (e.g.,
when to select or terminate it, and what actions it performs)
is represented in long-term memory by means of {\em productions}, 
similar to condition-action rules.
The condition part of a production specifies a pattern to be matched against
the current contents of working (short-term) memory, while the action part
specifies a set of actions, such as updates to working memory.  Soar's basic
control structure is to repeatedly attempt to select an operator and apply
it, based upon what productions have fired in response to the current
contents of working memory.  Note that operator execution time, in Soar's
theory of cognition, can be mapped to a constant amount of cognitive time.
Thus, it is possible to verify if a Soar model's temporal
performance is consistent with data about human performance.  

In addition to applying operators, the Soar architecture provides a basic
learning mechanism which can be used to acquire new operators.  (The learned
operators are also represented as productions stored in long-term memory.)
We have designed a method whereby discourse operators are
acquired by NL-Soar during discourse generation, 
and are used in both discourse generation and interpretation.  
(For a detailed description of our approach, see \cite{Green96}.)
For example, in generating a speaker's turn 
over a radio channel, NL-Soar may decide to begin by making a 
self-introduction using the speaker's radio call sign (e.g., {\em Dakota 204})
since the addressee might otherwise not be able to identify who is speaking.
The result of this planning process automatically results in a learned
discourse operator.
In subsequent situations with similar features, NL-Soar would be able
to generate a self-introduction using the learned operator rather than
by repeating the planning process.
Also, the same operator can be used during interpretation in similar 
discourse contexts to recognize the speaker's intention in uttering his 
radio call sign.

Thus, our current work in investigating the acquisition and use of
discourse operators serves as a step towards the goals 
of processing novel CI in a cognitively plausible computational model.
Furthermore, it may be possible to use NL-Soar to model
how certain CI become associated with certain linguistic forms.
NL-Soar learns syntactic and semantic operators while parsing
novel inputs.  These operators enable NL-Soar, on encountering similar
expressions, to comprehend them much more efficiently using the results
of its past experience instead of repeating the problem-solving process.
One way of modeling how certain CI become associated with certain linguistic
forms would be
by use of learned operators combining syntactic, semantic, and pragmatic 
processing.  NL-Soar's bias to use these learned operators before 
attempting problem-solving from first principles would 
account for the difficulty of recovering the
original meaning of such forms.
As for the first two goals, i.e. the goals of identifying the knowledge
and processes involved in use of CI, much work remains to be done.
However, the results of that work could be incorporated into a model
such as NL-Soar.  Furthermore, NL-Soar provides a basis for addressing
the last goal, by providing a model of natural language processing 
situated in a general model of human cognition.

\bibliography{/afs/cs.cmu.edu/user/ngreen/bibfile}
%\bibliography{/afs/cs.cmu.edu/project/soar/utc/nl/ngreen/bibfile}
\bibliographystyle{aaai}
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