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\title{Goals for Future Computational Models \\
of Conversational Implicature}
\author{Nancy Green and Jill Fain Lehman}
\date{October 31, 1995}
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One avenue of research on conversational implicature (CI) 
in non-figurative discourse which should be
addressed in the immediate future is to start implementing computational
models of agents who, in the course of real-time conversational 
interaction, generate and comprehend conversational implicatures.
This paper elaborates on this goal and describes
how our current work serves as a stepping stone towards such a model.

We believe that future computational
models of CI should aspire to meet the following goals:
\begin{itemize}
\item To be descriptively adequate models of CI.
      That is, 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 the cancelability as well as 
      the loss of cancelability of CI's in different portions of
      the same dialogue.
      To be capable of handling
      different types of CI without unwarranted conflict.
\item To embed the interpretation/generation of CI in a general
      computational model of conversational interaction such that
      the model accounts for why/when a speaker is motivated to use CI,
      and when/how a hearer derives a CI.  
\item To be a cognitively plausible model.  That is, to be subject
      to real-time performance constraints and human resource limitations.
\item To model diachronically how certain CI become semi-conventional, 
      e.g., the association of particular linguistic forms with certain CI,
      making it problematic to use those forms to convey just their
      original meaning.
\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 lead to new predictions about and insights into linguistic
      systems (including discourse) and how they interact 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}

The first of the above goals describes what we mean by a model of 
conversational implicature, as opposed to other types of discourse phenomena.
We claim that the  second goal is necessary for a descriptively adequate 
{\em computational} model of CI.
Moreover, unless the model of CI is implemented as
part of a general model of conversation, it would be 
hard to evaluate its success or limitations.
The goal of cognitive plausibility is desirable for several practical
reasons.
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 modeling how certain CI become semi-conventional
and of learning to interpret novel CI 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 scientific 
discovery, but also to enable our models to better approximate human
competence in the long term.  For example, for a first approximation,
a model of certain 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.

Given the above goals, we believe that there are several advantages to
building models of CI within more general models such as 
NL-Soar.\footnote{Note that we are {\em not} claiming that NL-Soar's (or 
Soar's) approach is the {\em only} approach having these properties.}
Before supporting this claim, first, we describe NL-Soar.
NL-Soar is a computational model of real-time natural language generation
and comprehension implemented within the Soar 
architecture.  (Soar is an architecture which has been proposed 
as a candidate model of general human cognition, 
and which has been implemented to provide a
computational system for cognitive modeling.\footnote{For 
more information on Soar itself, see \cite{Newell,Gentle-Soar} 
or the Soar home page at
http://www.isi.edu/soar/soar.html.})
Previous versions of NL-Soar focused on the problems of real-time 
parsing \cite{Lewis93} and sentence generation 
\cite{Rubinoff93}.  Current areas of development include
speech recognition, lexical semantics, integrating mechanisms used
in sentence generation with those used in sentence comprehension,
and discourse modeling, with the goal of building a system which
can participate in conversation with humans or with other simulated
agents within a task domain.  

Most linguistic (including pragmatic) knowledge is represented in NL-Soar
as operators which are applied to states to achieve goals, 
i.e. desired states.\footnote{Other linguistic knowledge is represented
as elaborations and search control knowledge.  Elaborations specify
aspects of the current situation which are contingent upon other aspects
of the current situation.  Search control knowledge is used to guide
selection of operators.}
For example, NL-Soar provides an operator, {\em acquire-turn}, to model
the acquisition of the turn by a conversational participant.
Language processing is driven by the current state of the situation model,
which is a representation of relevant contextual knowledge (e.g., perceptual
knowledge, intermediate linguistic representations, discourse context,
and world knowledge retrieved from long-term memory).\footnote{Note 
that Soar supports an integrated representation of linguistic
and nonlinguistic behavior, i.e., all permanent knowledge is represented
uniformly, and all processing is driven by the current situation.}  
During the application of an operator, NL-Soar may create subgoals.
Thus, a problem may be solved by decomposition into a hierarchy of
subgoals.  An important property of the Soar architecture is that 
the act of problem-solving via subgoaling has a persistent effect upon
the cognitive model, i.e., the model thereby acquires knowledge (``chunks'')
which may be applied in future situations.\footnote{``Chunks'' are 
encoded in the same (symbolic) format as knowledge provided by the model's
builder, and are open to inspection by the builder as well.} 
For example, in generating a speaker's turn over a radio channel, 
NL-Soar may pursue a hierarchy of subgoals resulting in the speaker's 
decision to begin by making a self-introduction (e.g., {\em This is
Dakota 204})
since the addressee might otherwise not be able to identify who is speaking.
In subsequent situations with similar features, NL-Soar would be able
to generate a self-introduction without subgoaling.
Thus, learning is a continual side-effect of problem-solving.  
Finally, it should be noted that operator execution time in the Soar
architecture can be used to verify if a Soar model's temporal
performance is consistent with data about human performance.  

Given the above goals, it would be advantageous to
build models of CI within a model such as NL-Soar
for the following reasons.  
First, doing so addresses the second goal.\footnote{Conversely,
for NL-Soar to provide an adequate model of conversational behavior, 
it should incorporate a model of CI.}  Moreover, it enables the model of
CI to share mechanisms used in other processes,
linguistic or non-linguistic.
Second, to implement a model of CI within NL-Soar imposes the constraint
of cognitive plausibility, i.e., addresses the third goal.
For example, a crucial problem in interpreting CI in real-time
is the control of inference, i.e., not wasting too much time on inferences
which do not contribute to the comprehension of the CI. 
The Soar architecture permits inference to be constrained by knowledge.
More specifically, persistent modifications of an agent's situation model
can be achieved only through the application of an operator.
In other words, the application of an operator specifies all and only 
the inferences, i.e. updates to the agent's current situation model,
licensed in that context.  The architecture permits only one operator 
to be applied at a time.  If more than one operator is eligible to apply 
in the current situation, then explicit knowledge can be used to select 
an operator, i.e., to decide which operator to apply.  
This search control knowledge can be encoded
by the model's builder or learned by the model itself.

It may be feasible to exploit NL-Soar's learning mechanism to address the
next two goals.  For example, currently NL-Soar learns syntactic and
semantic comprehension
operators while parsing novel inputs.  These operators enable NL-Soar,
on encountering similar expressions, to parse 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 goal of interpreting novel types of CI without requiring the 
model builder to provide licensing rules specific to that class of CI,
consider how the first author's computational model for interpreting and 
generating indirect answers to Yes-No questions \cite{Green94}
could be extended to address this goal.  In that model, 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 her 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.  This knowledge source is used both in generation and interpretation,
and is used for modeling both direct and indirect answers.  In other words,
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.  

However, an
unfortunate limitation of that type of model is that the set of discourse plan
operators must be provided by the model's builder.  On the other hand,
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.  Currently, we are investigating how NL-Soar's learning mechanism
might be used, as an alternative to providing a hand-crafted library of
discourse plan operators, to acquire discourse planning knowledge which
could be used, not only for discourse generation, but also for discourse
comprehension.  For example, the process of planning to produce a
self-introduction results in the acquisition of a new, learned discourse
operator.  The same operator could be reused during interpretation
to recognize a speaker's intention in uttering his name.  Research 
within the Soar architecture on
use of the operators which an agent uses to generate his own behavior
for tracking other agents' behavior is described in
\cite{Tambe-ijcai95}.  We are currently investigating how to extend that
approach for sets of learned operators, which would enable learned
discourse planning operators to be used in discourse comprehension.

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