planning text for advisory dialogues

planning text for advisory dialogues
Johanna D. Moore
UCLA Department of Computer Science
USC/Information Sciences Institute
4676 Admiralty Way
Marina del Key, CA 90292-6695, USA
Explanation is an interactive process requiring a dialogue between advice-giver and
advice-seeker. In this paper, we argue that
in order to participate in a dialogue with its
users, a generation system must be capable of
reasoning about its own utterances and therefore must maintain a rich representation of
the responses it produces. We present a text
planner that constructs a detailed text plan,
containing the intentional, attentional, and
.,,e~,~nc~ ~tructures of the text it generates.
Providing explanations in an advisory situation is a highly interactive process, requiring
a dialogue between advice-giver and adviceseeker (Pollack e t a / . , 1982). Participating in
a dialogue requires the ability to reason about
previous responses, e.g., to interpret the user's
follow-up questions in the context of the ongoing conversation and to determine how to
clarify a response when necessary. To provide these capabilities, an explanation facility
must understand what it was trying to convey
and how that information was conveyed, i.e.,
the intentional structure behind the explanation, including t h e g o a l of the explanation as a
whole, the subgoal(s)of individual parts of the
explanation, and the rhetorical means used to
achieve them.
Researchers in natural language under.
standing have recognized the need for such
information. In their work on discourse analysis, Grosz and Sidner (1986) argue that it is
necessary to represent the intentional structure, the attentional structure (knowledge
about which aspects of a dialogue are in focus
at each point), and the linguistic structure of
"The research described in this paper was supported by the Defense Advanced Research Projects
Agency (DARPA) under a NASA Ames cooperative
agreement number NCC 2-520. The authors would
like to thank William Swartout for comments on earlier versions of this paper.
C~cile L. Paris
USC/information Sciences Institute
4676 Admiralty Way
Marina del Key, CA 90292-6695, USA
the discourse. In contrast, most text generation systems (with the notable exception of
KAMP (Appelt, 1985)) have used only rhetorical and attentional information to produce
coherent text (McKeown, 1985, McCoy, 1985,
Paris, 1988b), omitting intentional information, or conflating intentional and rhetorical
information (Hovy, 1988b). No text generation system records or reasons about the
rhetorical, the attentional, as well as the intentional structures of the texts it produces.
In this paper, we argue that to successfully participate in an explanation dialogue,
a generation system must maintain the kinds
of information outlined by Grosz and Sidner
as well as an explicit representation of the
rhetorical structure of the texts it generates.
We present a text planner that builds a detailed text plan, containing the intentional,
attentional, and rhetorical structures of the
responses it produces. The main focus of
this paper is the plan language and the plan
structure built by our system. Examples of
how this structure is used in answering followup questions appear in (Moore and S w a r t o u t ,
In order to handle follow-up questions that
may arise if the user does not fully understand
a response given by the system, a generation
facility must be able to determine what portion of the text failed to achieve its purpose. If
the generation system only knows the top-level
discourse goal that was being achieved by the
text (e.g., persuade the hearer to perform an
action), and not what effect the individual
parts of the text were intended to have on the
hearer and how they fit together to achieve
this top-level goal, its only recourse is to use a
different strategy to achieve the top-level goal.
It is not able to re-explain or clarify any part
of the explanation. There is thus a need for
a text plan to contain a specification of the
intended effect of individual parts of the text
on the hearer and how the parts relate to one
another. We have developed a text planner
that records the following information about
the responses it produces:
• the information that Grosz and Sidner
(1986) have presented as the basics of a
discourse structure:
i n t e n t i o n a l structure: a representation of the effect each part of
the text is intended to have on the
hearer and how the complete text
achieves the overall discourse purpose (e.g., describe entity, persuade
hearer to perform an action).
a t t e n t i o n a l structure: information
about which objects, properties and
events are salient at each point
in the discourse.
User's followup questions are often ambiguous.
Information about the attentional
state of the discourse can be used
to disambiguate them (cf. (Moore
and Swartout, 1989)).
• in addition, for generation we require the
rhetorical structure: an agent must
understand how each part of the
text relates rhetorically to the others. This is necessary for linguistic reasons (e.g., to generate the
appropriate clausal connectives in
multi-sentential responses) and for
responding to requests for elaboration/clarification.
• a s s u m p t i o n information:
ad'vicegiving systems must take knowledge about their users into account.
However, since we cannot rely on
having complete user models, these
systems may have to make assumptions about the hearer in order to
use a particular explanation strategy. Whenever such assumptions
are made, they must be recorded.
The next sections describe this new text planner and show how it records the information
needed to engage in a dialogue. Finally, a brief
comparison with other approaches to text generation is presented.
The text planner has been developed as part
of an explanation facility for an expert sys204
tern built using the Explainable Expert Systems (EES) framework (Swartout and Smoliar, 1987). The text planner has been used
in two applications. In this paper, we draw
our examples from one of them, the Program
Enhancement Advisor (PEA) (Neches et al.,
1985). PEA is an advice-giving system intended to aid users in improving their Common Lisp programs by recommending transformations that enhance the user's code. 1 The
user supplies PEA with a program and indicates which characteristics of the program
should be enhanced (any combination of readability, maintainability, and efficiency). PEA
then recommends transformations. After each
recommendation is made, the user is free to
ask questions about the recommendation.
We have implemented a top-down hierarchical expansion planner (d la Sacerdoti
(1975)) that plans utterances to achieve discourse goals, building (and recording) the intentional, attentional, and rhetorical structure of the generated text. In addition, since
the expert system explanation facility is intended to be used by many different users,
the text planner takes knowledge about the
user into account. In our system, the user
model contains the user's domain goals and
the knowledge he is assumed to have about
the domain.
In our plan language, intentional goals are
represented in terms of the effects the speaker
intends his utterance to have on the hearer.
Following Hovy (1988a), we use the terminology for expressing beliefs developed by Cohen
and Levesque (1985) in their theory of rational interaction, but have found the need to
extend the terminology to represent the types
of intentional goals necessary for the kinds
of responses desired in an advisory setting.
Although Cohen and Levesque have subsequently retracted some aspects of their theory
of rational interaction (Cohen and Levesque,
1987), the utilityof their notation for our purposes remains unaffected, as argued in (Hovy,
aPEA recommends transformations that improve
the 'style' of the user's code. It does not attempt to
understand the content of the user's program.
2Space limitations prohibit an exposition of their
terminology in this paper. We provide English paraphrases where necessary for clarity.
(BR8 S II x)
should be read as 'the speaker believes the speaker
and hearer mutually believe x.'
EFFECT: (PERSUADE S H (GOAL H Eventually(DONE H ?act)))
CONSTRAINTS: (AND (GOAL S ?domain-goal)
(STEP ?act ?domain-goal)
(BMB S H (GOAL H ?domaln-goal)))
NUCLEUS: (FOR.ALL ?domain-goal
(MOTIVATION ?act ?domain-goal))
Figure 1: Plan Operator for Persuading the Hearer to Do An Act
EFFECT: ( M O T I V A T I O N ?act ?domain-goal)
CONSTRAINTS: ( A N D ( G O A L S ?domain-goal)
(STEP ?act ?domain-goal)
( B M B S H (GOAL H ?domain-goal))
(ISA ?act R E P L A C E ) )
NUCLEUS: ((SETQ ?replacee (FILLER-OF O B J E C T ?act))
(SETQ ?replacer (FILLER-OF G E N E R A L I Z E D - M E A N S
( B M B S H ( D I F F E R E N C E S ?repLacee ?repLacer ?domain-goal)) )
Figure 2: Plan Operator for Motivating a Replacement by Describing Differences between Replacer
and Replacee
Rhetorical structure is represented in
terms of the rhetorical relations defined in
Rhetorical Structure Theory (RST) (Mann
and Thompson, 1987), a descriptive theory
characterizing text structure in terms of the
relations that hold between parts of a text
(e.g., CONTRAST, MOTIVATION). The definition of each R S T relation includes constraints
on the two entities being related as well as
constraints on their combination, and a specification of the effect which the speaker is
attempting to achieve on the hearer's belids. Although other researchers have categorized typical intersentential relations (e.g.,
(Grimes, 1975, Hobbs, 1978)), the set of relations proposed by R S T is the most complete
and the theory sufficientlydetailed to be easily adapted for use in generation.
In our plan language, each plan operator
consists of:
an effect:
a characterization of what
goai(s) this operator can be used to
A n effect m a y be an intentional goal, such as persuade the
h e a r e r t o do an ac~ionorarhetorical
relation, such as provide motivation
for an action.
a constraint list: a list of conditions that
must be true before the operator can be
applied. Constraints m a y refer to facts
in the system's knowledge base or in the
user model.
the main topic to be expressed. The nucleus is either a primitive operator (i.e., speech acts such as
inform, recommend and ask) or a goal
intentional or rhetorical) which must be
ther expanded. All operators must
contain a nucleus.
• a nucleus:
subgoal(s)that express additional information which may be needed
to achieve the effect of the operator.
When present, satellites may be specified
as required or optional.
• satellites:
Examples of our plan operators are shown
in Figures 1 and 2. The operator shown in
Figure 1 can be used if the speaker (S) intends
to persuade the hearer (H) to intend to do
some act. This plan operator states that if an
act is a step in achieving some domain goal(s)
that the hearer shares, one way to persuade
the hearer to do the act is to motivate the act
in terms of those domain goals. Note that this
plan operator takes into account not only the
system's knowledge of itself, but also the system's knowledge about the user's goals, as embodied in a user model. If any domain goals
that satisfy the constraints are found, this operator will cause the planner to post one or
more MOTIVATION subgoals. This plan operator thus indicates that one way to achieve the
intentional goal of persuading the hearer to
perform an action is by using the rhetorical
EFFECT: ( B M B S H ?x)
NUCLEUS: ( I N F O R M S H ?x)
SATELLITES: ( ( ( P E R S U A D E S H 7x) *optional*))
Figure 3: Plan Operator for Achieving Mutual Belief of a Proposition
What characteristicsof the program would you liketo enhance?
You should replace (setq x I) with (serf x I). Serf can be used to assign a
value to any generalized-variable. Serq can only be used to assign a value to a
simple-variable. A generalized-variableis a storage location that can be named by
any accessor function.
What is a generalized variable?
For example, the car and cdr of a cons are generalized-variables,named by the
accessor functions car and cdr. Other examples are an element of an array or a
component of a structure.
Figure 4: Sample Dialogue
Plans that achieve intentional goals and
those that achieve rhetorical relations are distinguished for two reasons: (1) so that the
completed plan structure contains both the intentional goals of the speaker and the rhetorical means used to achieve them; (2) because
there are m a n y different rhetorical strategies
for achieving any given intentional goal. For
example, the system has several plan operators for achieving the intentional goal of describing a concept. It m a y describe a concept
by stating its class membership and describing its attributes and its parts, by drawing
an analogy to a similar concept, or by giving
examples of the concept. There m a y also be
m a n y different plan operators for achieving
a particular rhetorical strategy. (The planner employs selection heuristics for choosing
among applicable operators in a given situation (Moore and Swartout, 1989).)
Our plan language allows both general
and specific plans to be represented. For example, Figure 2 shows a plan operator for
achieving the rhetorical relation MOTIVATION.
This is a very specific operator that can be
used only when the act to be motivated is a
replacement (e.g., replace sezq with s e z f ) .
In this case, one strategy for motivating the
act is to compare the object being replaced
and the object that replaces it with respect
to the domain goal being achieved. On the
other hand, the operator shown in Figure 3
is general and can be used to achieve mu206
tual belief of any assertion by first informing the hearer of the assertion and then, optionaUy, by persuading him of that fact. Because we allow very general operators as well
as very specific ones, we can include both
domain-independent and domain-dependent
Consider the sample dialogue with our system shown in Figure 4, in which the user indicates that he wishes to enhance the maintainability of his program. While enhancing maintainability, the system recommends
that the user perform the act r e p l a c e - I ,
namely 'replace setq with serf', and thus
posts the intentional goal (BMB S H (GOAL
H Evenzually(DONE H replace-I))). This
discourse goal says that the speaker would like
to achieve the state where the speaker believes
that the hearer and speaker mutually believe
that it is a goal of the hearer that the replacement eventually be done by the hearer.
The planner then identifies all the operators whose effect field matches the discourse
goal to be achieved. For each operator found,
the planner checks to see if all of its constraints are satisfied. In doing so, the text
planner attempts to find variable bindings in
the expert system's knowledge base or the
user model that satisfy all the constraints in
EFFECT: ( B M B S H ( G O A L H Eventually(DONE H ?act)))
S H ?act)
H ( D O N E H ?act))) *optional*)
((PERSUADE S H (GOAL H Eventually(DONE H 7act))) *optional*) )
Figure 5: High-level Plan Operator for Recommending an Act
apply-SETQ-t o-SETF-~rans formal;ion
apply-lo cal-1;ransf ormat ions-whos e-rhs-us e-is-mor e-general-1:han-lhs-us •
apply-local-1;rans f orma1~ions-thal;-enhance-mainl;ainability
apply-1~ransforma¢ ions-1~hal;-enhance-mainl;ainabili~y
enhanc e-mainl;ainabili1:y
Figure 6: System goals leading to r e p l a c e s e t q wil;h sel;f
the constraint list. Those operators whose
constraints are satisfied become candidates for
achieving the goal, and the planner chooses
one based on: the user model, the dialogue
history, the specificity of the plan operator,
and whether or not assumptions about the
user's beliefs must be made in order to satisfy
the operator's constraints.
Continuing the example, the current discourse goal is to achieve the state where
it is mutually believed by the speaker and
hearer that the hearer has the goal of eventually executing the replacement. This discourse goal can be achieved by the plan operator in Figure 5. This operator has no
Assume it is chosen in this
case. The nucleus is expanded first, 3 causing
(RECOMMEND S H replace-l) to be posted as
a subgoal. RECOMMEND is a primitive operator,
and so expansion of this branch of the plan is
complete. 4
Next, the planner must expand the satellites. Since both satellitesare optional in this
case, the planner must decide which, if any,
are to be posted as subgoals. In this example,
the firstsatellitewill not be expanded because
the user model indicates that the user is ca31n some cases, such as a satelliteposting the
rhetorical relation background, the satelliteis expanded first.
+At thispoint, (RECOMMEND S H replace-l) must
be translatedinto a form appropriateas the
realization component, the Penman system (Mann,
1983, Kasper, 1989). Based on the type of speech act,
its arguments, and the context in which it occurs, the
planner builds the appropriate structure. Bateman
and Paxis (1989) have begun to investigatethe problem of phrasing utterancesfor differenttypes of users.
pable of performing replacement acts. The
second satellite is expanded, s posting the intentional subgoal to persuade the user to perform the replacement. A plan operator for
acldeving this goal using the rhetorical relation MOTIVATION was shown in Figure i.
W h e n attempting to satisfy the constraints of the operator in Figure 1, the
system first checks the constraints (GOAL
S ?domain-goal)
(STEP replace-1
?domain-goal). These constraints state that,
in order to use this operator, the system must
find an expert system goal, ?domain-goal,
that replace-I is a step in achieving.
This results in several possible bindings
for the variable ?domain-goal. In this case,
the applicable system goals, listed in order
from most specific to the top-level goal of the
system, are shown in Figure 6.
The last constraint of this plan operator, (BMB S H (GOAL H ?domain-goal)), is
a constraint on the user model stating that the
speaker and hearer should mutu~IIy believe
that ?domain-goal is a goal of the hearer.
Not all of the bindings found so far will satisfy this constraint. Those which do not will
not be rejected immediately, however, as we
do not assume that the user model is complete. Instead, they will be noted as possible
bindings, and each will be marked to indicate
that, if this binding is used, an assumption
is being made, namely that the binding of
Sin other situations, the system could choose not
to expand this satellite and await feedback from the
user instead (Moore and Swartout, 1989).
(BMB S H (GOAL H Eventually (DONE H replace-I)))
(MOTIVATION replace1 enhance-maintainability)
(RECOMMENDS H replace-I)
(PERSUADES H (GOAL H Eventually (DONE H replace-I)))
(BMB S H (DIFFERENCESsetq serf enhance-maintainability))
(MOTIVATION replace-1 enhance-maintainability)
(BMBS H (DIFFERENCEsetq serf use))
(BMR S H (KNOW H generalized-variable))
(ELABORATIONgeneral zed-variable)
generalized-variable storage-location))
repla(el = replm:eSETQwithSETF
N • Nucleus
S = Satellite
generalized-variable named-by)
(VALUE-OF named-by accessor-function )))
Figure 7: Completed Text Plan for Recommending Replace S E T Q with S E T F
?domain-goal is assumed to be a goal of the
In this example, since the user is using
the system to enhance a program and has indicated that he wishes to enhance the maintainability of the program, the system infers
the user shares the top-levelgoal of the system
(enhance-program), as well as the more specificgoal enhance-mainZainabilizy. Therefore, these are the two goals that satisfy the
constraints of the operator shown in Figure I.
The text planner prefers choosing binding
environments that require no assumptions to
be made. In addition, in order to avoid explaining parts of the reasoning chain that the
user is familiar with, the most specific goal is
chosen. The plan operator is thus instantiated with enhance-mainzainability as the
binding for the variable ?domain-goal. The
selected plan operator is recorded as such, and
all other candidate operators are recorded as
untried alternatives.
The nucleus of the chosen plan operator is now posted, resulting in the
subgoal (MOTIVATION replace-1 enhancemainZainability). The plan operator chosen for achieving this goal is the one that
was shown in Figure 2. This operator motivates the replacement by describing differences between the object being replaced and
the object replacing it. Although there are
many differences between sezq and serf,
only the differences relevant to the domain
goal at hand (enhance-mainzainabilizy)
should be expressed. The relevant differences are determined in the following way.
From the expert system's problem-solving
knowledge, the planner determines what roles
eezq and e e z f play in achieving the goal
enhance-maintainabilizy. In this case, the
system is enhancing maintainability by applying transformations that replace a specific
construct with one that has a more general
usage. SeZq has a more specific usage than
sezf, and thus the comparison between sezq
and sezf should be based on the generality of
their usage.
Finally, since the term g e n e r a l i z e d v a r i a b l e has been introduced, and the
user model indicates that the user does
not know this term, an intentional goal
to define it is posted:
H generalized-variable)).
This goal is
achieved with a plan operator that describes
concepts by stating their class membership
and describing their attributes. Once completed, the text plan is recorded in the dialogue history. The completed text plan for
response (3) of the sample dialogue is shown
in Figure 7.
As illustrated in Figure 7, a text plan produced by our planner provides a detailed representation of the text generated by the system, indicating which purposes different parts
of the text serve, the rhetorical means used
to achieve them, and how parts of the plan
are related to each other. The text plan also
contains the assumptions that were made during planning. This text plan thus contains
both the intentional structure and the rhetorical structure of the generated text. From
this tree, the dominance and saris/actionprecedence relationships as defined by Grosz
and Sidner can be inferred. Intentional goals
higher up in the tree dominate those lower
down and a left to right traversal of the
tree provides satisfaction-precedence ordering.
The attentional structure of the generated
text can also be derived from the text plan.
The text plan records the order in which topics appear in the explanation. The global variable *local-contezt ~ always points to the plan
node that is currently in focus, and previously
focused topics can be derived by an upward
traversal of the plan tree.
The information contained in the text
plan is necessary for a generation system to be
able to answer follow-up questions in context.
Follow-up questions are likely to refer to the
previously generated text, and, in addition,
they often refer to part of the generated text,
as opposed to the whole text. Without an explicit representation of the intentional structure of the text, a system cannot recognize
that a follow-up question refers to a portion of
the text already generated. Even if the system
realizes that the follow-up question refers back
to the original text, it cannot plan a text to
clarify a part of the text, as it no longer knows
what were the intentions behind various pieces
of the text.
Consider again the dialogue in Figure 4.
When the user asks 'What is a generalized variable?'
(utterance (4) in Figure 4), the query analyzer interprets this question and posts the goal: (BMB S H (KNOW H
g e n e r a l i z e d - v a r i a b l e ) ). At this point, the
explainer must recognize that this discourse
goal was attempted and not achieved by the
last sentence of the previous explanation. 6
Failure to do so would lead to simply repeating the description of a generalized variable
that the user did not understand. By examining the text plan of the previous explanation
recorded in the dialogue history, the explainer
is able to determine whether the current goal
(resulting from the follow-up question) is a
goal that was attempted and failed, as it is
in this case. This time, when attempting to
achieve the goal, the planner must select an alternative strategy. Moore (1989b) has devised
recovery heuristics for selecting an alternative
strategy when responding to such follow-up
questions. Providing an alternative explanation would not be possible without the explicit
representation of the intentional structure of
t h e generated text. Note that it is important
to record the rhetorical structure as well, so
that the text planner can choose an alternative rhetorical strategy for achieving the goal.
In the example under consideration, the recovery heuristics indicate that the rhetorical
strategy of giving examples should be chosen.
Schemata (McKeown, 1985) encode standard
patterns of discourse structure,but do not indude knowledge of how the various parts of
a schema relate to one another or what their
intended effect on the hearer is. A schema
can be viewed as a compiled version of one
of our text plans in which all of the nonterminal nodes have been pruned out and only
t h e leaves (the speech acts) remain. While
schemata can produce the same initial behavior as one of our text plans, all of the rationale for that behavior has been compiled out.
Thus schemata cannot be used to participate
in dialogues. If the user indicates that he has
n o t understood the explanation, the system
cannot know which part of the schema failed
to achieve its effect on the hearer or which
rhetorical strategy failed to achieve this effect. Planning a text using our approach is
essentially planning a: schema from more finegrained plan operators. From a library of such
plan operators, many varied schemata can result, improving the flexibility of the system.
In an approach taken by Cohen and Appelt (1979) and Appelt (1985), text is planned
by reasoning about the beliefs of the hearer
and speaker and the effects of surface speech
aWe are also currently i m p l e m e n t i n g another interface which allows users to use a mouse to point at
the noun phrases or clauses in the text that were not
understood {Moore, 1989b).
acts on these beliefs (i.e., the intentional effect). This approach does not include rhetorical knowledge about how clausal units may be
combined into larger bodies of coherent text
to achieve a speaker's goals. It assumes that
appropriate axioms could be added to generate large (more than one- or two-sentence)
bodies of text and that the text produced will
be coherent as a by-product of the planning
process. However, this has not been demonstrated.
Itecently, Hovy (1988b) built a text structurer which produces a coherent text when
given a set of inputs to express. Hovy uses
an opportunistic planning approach that orders the inputs according to the constraints
on the rhetorical relations defined in Rhetorical Structure Theory. His approach provides a
description of what can be said when, but does
not include information about why this information can or should be included at a particular point. Hovy's approach confiates intentional and rhetorical structure and, therefore,
a system using his approach could not later
reason about which rhetorical strategies were
used to achieve intentional goals.
We are also investigating criteria for the expansion and ordering of optional satellites in
our plan operators. Currently we use information from the user model to dictate whether
or not optional satellites are expanded, and
their ordering is specified in each plan operator. We wish to extend our criteria for satellite expansion to include other factors such as
pragmatic and stylistic goals (Hovy, 1988a)
(e.g., brevity) and the conversation that has
occurred so far. We are also investigating the
use of attentional information to control the
ordering of these satellites (McKeown, 1985).
We also believe that the detailed text plan
constructed by our planner will allow a system
to modify its strategies based on experience
(feedback from the user). In (Paris, 1988a),
we outline our preliminary ideas on this issue.
We have also begun to study how our planner
can be used to handle incremental generation
of texts. In (Moore, 1988), we argue that the
detailed representation provided by our text
plans is necessary for execution monitoring
and to indicate points in the planning process
where feedback from the user may be helpful
in incremental text planning.
The text planner presented is imple.mented
in C o m m o n Lisp and can produce the text
plans necessary, to participate in the sample
~lialogue described m this p a p e r and several
others (see (Moore, 1989a, Paris, 1988a)).
currently have over 60 plan operators a n d
the system can answer tlie following types of
(follow-up) questions:
- Why?
- Why conclusion?
Why are you trying to achieve goal?
Why are you using method to achieve goal?
Why are you doing act?
How do you achieve goal?
How did you achieve goal (in this case)?
What is a concept?
What is the difference between concept1
W e
In this paper, we have presented a text planner that builds a detailed text plan, containing the intentional, attentional, and rhetorical structures of the responses it produces.
We argued that, in order to participate in a
dialogue with its users, a generation system
must be capable of reasoning about its past
utterances. The text plans built by our text
planner provide a generator with the information needed to reason about its responses.
We illustrated these points with a sample dialogue.
and concept2?
H u h ?
The text planning system described in this
paper is being incorporated into two expert
systems currently under development. These
systems will be installed and used in the field.
This will give us an opportunity to evaluate
the techniques proposed here.
We are currently studying how the attentional structure inherent in our text plans can
be used to guide the realization process, for
example in the planning of referring expressions and the use of cue phrases and pronouns.
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