Investigating Cue Selection and Placement in Tutorial Discourse 
Megan Moser 
Learning Research g: Dev. Center, 
and Department of Linguistics 
University of Pittsburgh 
Pittsburgh, PA 15260 
moser@isp, pitt. edu 
Johanna D. Moore 
Department of Computer Science, and 
Learning Research & Dev. Center 
University of Pittsburgh 
Pittsburgh, PA 15260 
jmoore @ cs. pitt. edu 
Abstract 
Our goal is to identify the features that pre- 
dict cue selection and placement in order 
to devise strategies for automatic text gen- 
eration. Much previous work in this area 
has relied on ad hoc methods. Our coding 
scheme for the exhaustive analysis of dis- 
course allows a systematic evaluation and 
refinement of hypotheses concerning cues. 
We report two results based on this anal- 
ysis: a comparison of the distribution of 
Sn~CE and BECAUSE in our corpus, and the 
impact of embeddedness on cue selection. 
Discourse cues play a crucial role in many dis- 
course processing tasks, including plan recogni- 
tion (Litman and Allen, 1987), anaphora resolu- 
tion (Gross and Sidner, 1986), and generation of 
coherent multisentential texts (Elhadad and McK- 
eown, 1990; Roesner and Stede, 1992; Scott and 
de Souza, 1990; Zukerman, 1990). Cues are words 
or phrases such as BECAUSE, FIRST, ALTHOUGH and 
ALSO that mark structural and semantic relation- 
ships between discourse entities. While some specific 
issues concerning cue usage have been resolved (e.g., 
the disambiguation of discourse and sentential cues 
(Hirschberg and Litman, 1993)), our concern is to 
identify general strategies of cue selection and place- 
ment that can be implemented for automatic text 
generation. Relevant research in reading comprehen- 
sion presents a mixed picture (Goldman and Mur- 
ray, 1992; Lorch, 1989), suggesting that felicitous 
use of cues improves comprehension and recall, but 
that indiscriminate use of cues may have detrimental 
effects on recall (Millis et al., 1993) and that the 
benefit of cues may depend on the subjects' reading 
skill and level of domain knowledge (McNamara et 
al., In press). However, interpreting the research is 
problematic because the manipulation of cues both 
within and across studies has been very unsystem- 
atic (Lorch, 1989). While Knott and Dale (1994) 
use systematic manipulation to identify functional 
categories of cues, their method does not provide 
the description of those functions needed for text 
generation. 
For the study described here, we developed a cod- 
ing scheme that supports an exhaustive analysis of 
a discourse. Our coding scheme, which we call Re- 
lational Discouse Analysis (RDA), synthesizes two 
accounts of discourse structure (Gross and Sidner, 
1986; Mann and Thompson, 1988) that have often 
been viewed as incompatible. We have applied RDA 
to our corpus of tutorial explanations, producing an 
exhaustive analysis of each explanation. By doing 
such an extensive analysis and representing the re- 
sults in a database, we are able to identify patterns 
of cue selection and placement in terms of multiple 
factors including segment structure and semantic re- 
lations. For each cue, we determine the best descrip- 
tion of its distribution in the corpus. Further, we are 
able to formulate and verify more general patterns 
about the distribution of types of cues in the corpus. 
The corpus study is part of a methodology for 
identifying the factors that influence effective cue 
selection and placement. Our analysis scheme is co- 
ordinated with a system for automatic generation of 
texts. Due to this coordination, the results of our 
analyses of "good texts" can be used as rules that 
are implemented in the generation system. In turn, 
texts produced by the generation system provide a 
means for evaluation and further refinement of our 
rules for cue selection and placement. Our ultimate 
goal is to provide a text generation component that 
can be used in a variety of application systems. In 
addition, the text generator will provide a tool for 
the systematic construction of materials for reading 
comprehension experiments. 
The study is part of a project to improve the 
explanation component of a computer system that 
trains avionics technicians to troubleshoot complex 
electronic circuitry. The tutoring system gives the 
student a troubleshooting problem to solve, allows 
the student to solve the problem with minima\] tutor 
interaction, and then engages the student in a post- 
problem critiquing session. During this session, the 
system replays the student's solution step by step, 
pointing out good aspects of the solution as well 
as ways in which the solution could be improved. 
130 
To determine how to build an automated explana- 
tion component, we collected protocols of 3 human 
expert tutors providing explanations during the cri- 
tiquing session. Because the explanation component 
we are building interacts with users via text and 
menus, the student and human tutor were required 
to communicate in written form. In addition, in or- 
der to study effective explanation, we chose experts 
who were rated as excellent tutors by their peers, 
students, and superiors. 
1 Relational Discourse Analysis 
Because the recognition of discourse coherence and 
structure is complex and dependent on many types 
of non-linguistic knowledge, determining the way in 
which cues and other linguistic markers aid that 
recognition is a difficult problem. The study of cues 
must begin with descriptive work using intuition and 
observation to identify the factors affecting cue us- 
age. Previous research (Hobbs, 1985; Grosz and 
Sidner, 1986; Schiffrin, 1987; Mann and Thomp- 
son, 1988; Elhadad and McKeown, 1990) suggests 
that these factors include structural features of the 
discourse, intentional and informational relations in 
that structure, givenness of information in the dis- 
course, and syntactic form of discourse constituents. 
In order to devise an algorithm for cue selection and 
placement, we must determine how cue usage is af- 
fected by combinations of these factors. The corpus 
study is intended to enable us to gather this infor- 
mation, and is therefore conducted directly in terms 
of the factors thought responsible for cue selection 
and placement. Because it is important to detect 
the contrast between occurrence and nonoccurrence 
of cues, the corpus study must be be exhaustive, 
i.e., it must include all of the factors thought to 
contribute to cue usage and all of the text must be 
analyzed. From this study, we are deriving a system 
of hypotheses about cues. 
In this section we describe our approach to the 
analysis of a single speaker's discourse, which we call 
Relational Discourse Analysis (RDA). Apply- 
ing RDA to a tutor's explanation is exhaustive, i.e., 
every word in the explanation belongs to exactly one 
element in the analysis. All elements of the analysis, 
from the largest constituents of an explanation to 
the minimal units, are determined by their function 
in the discourse. A tutor may offer an explanation 
in multiple segments, the topmost constituents of 
the explanation. Multiple segments arise when a 
tutor's explanation has several steps, e.g., he may 
enumerate several reasons why the student's action 
was inemcient, or he may point out the flaws in the 
student's step and then describe a better alterna- 
tive. Each segment originates with an intention of 
the speaker; segments are identified by looking for 
sets of clauses that taken together serve a purpose. 
Segments are internally structured and consist of a 
core, i.e., that element that most directly expresses 
the segment purpose, and any number of contrlb- 
utors, the remaining constituents in the segment 
each of which plays a role in serving the purpose 
expressed by the core. For each contributor in a 
segment, we analyze its relation to the core from 
an intentional perspective, i.e., how it is intended to 
support the core, and from an informational perspec- 
tive, i.e., how its content relates to that of the core. 
Each segmei,t constituent, both core and contribu- 
tors, may itself be a segment with a core:contributor 
structure, or may be a simpler functional element. 
There are three types of simpler functional elements: 
(1) units, which are descriptions of domain states 
and actions, (2) matrix elements, which express a 
mental attitude, a prescription or an evaluation by 
embedding another element, and (3) relation clus- 
ters, which are otherwise like segments except that 
they have no core:coatributor structure. 
This approach synthesizes ideas which were pre- 
viously thought incompatible from two theories of 
discourse structure, the theory proposed by Grosz 
and Sidner (1986) and Rhetorical Structure Theory 
(RST) proposed by Mann and Thompson (1988). 
The idea that the hierarchical segment structure of 
discourse originates with intentions of the speaker, 
and thus the defining feature of a segment is that 
there be a recognizable segment purpose, is due 
to Grosz and Sidner. The idea that discourse is 
hierarchically structured by palrwise relations in 
which one relatum (the nucleus) is more central to 
the speaker's purpose is due to Mann and Thomp- 
son. Work by Moore and Pollack (1992) modi- 
fied the RST assumption that these palrwise re- 
lations are unique, demonstrating that intentional 
and informational relations occur simultaneously. 
Moser and Moore (1993) point out the correspon- 
dence between the relation of dominance among 
intentions in Grosz and Sidner and the nucleus- 
satellite distinction in RST. Because our analysis 
realizes this relation/distinction in a form different 
from both intention dominance and nuclearity, we 
have chosen the new terms core and contributor. 
To illustrate the application of RDA, consider the 
partial tutor explanation in Figure i t. The purpose 
of this segment is to inform the student that she 
made the strategy error of testing inside paxt3 too 
soon. The constituent that expresses the purpose, in 
this case (B), is the core" of the segment. The other 
constituents help to achieve the segment purpose. 
We analyze the way in which each contributor relates 
to the core from two perspectives, intentional and in- 
formational, as illustrated below. Each constituent 
may itself be a segment with its own core:contributor 
structure. For example, (C) is a subsegment whose 
tin order to make the example more intelligible to 
the reader, we replaced references to parts of the circuit 
with the simple labels partl, part~ and part3. 
131 
purpose is to give a reason for testing part2 first, 
namely that part2 is more susceptible to damage 
and therefore a more likely source of the circuit fault. 
The core of this subsegment is (C.2) because it most 
directly expresses this purpose. The contributor in 
(C.1) provides a reason for this susceptibility, i.e., 
that part2 is moved frequently. 
ALTHO 
A. you know that part1 is good, 
B. you should eliminate part2 
before troubleshooting in part3. 
THIS IS BECAUSE 
C. 1. part2 is moved frequently 
AND THUS 
2. is more susceptible to damage. 
Figure 1: An example tutor explanation 
Due to space limitations, we can provide only a 
brief description of core:contributor relations, and 
omit altogether the analysis of the example into 
the minimal RDA units of state and action units, 
matrix expressions and clusters. A contributor is 
analyzed for both its intentional and informational 
relations to its core. Intentional relations describe 
how a contributor may affect the heater's adoption 
of the core. For example, (A) in Figure 1 acknowl- 
edges a fact that might have led the student to make 
the mistake. Such a concession contributes to the 
hearer's adoption of the core in (B) by acknowledg- 
ing something that might otherwise interfere with 
this intended effect. Another kind of intentional re- 
lation is evidence, in which the contributors are 
intended to increase the hearer's belief in the core. 
For example, (C) stands in the evidence relation to 
(B). The set of intentional relations in RDA is a 
modification of the presentational relations of RST. 
Each core:contributor pair is also analyzed for its 
informational relation. These relations describe how 
the situations referred to by the core and contributor 
are related in the domain. 
The RDA analysis of the example in Figure 1 is 
shown schematically in Figure 2. As a convention, 
the core appears as the mother of all the relations it 
participates in. Each relation is labeled with both 
its intentional and informational relation, with the 
order of relata in the label indicating the linear order 
in the cliscourse. Each relation node has up to two 
daughters: the cue, if any, and the contributor, in 
the order they appear in the discourse. 
2 Reliability of RDA application 
To assess inter-coder reliability of RDA analyses, 
we compared two independent analyses of the same 
data. Because the results reported in this paper de- 
pend only on the structural aspects of the analysis, 
our reliability assessment is confined to these. The 
conce$$ton:core 
step :prev-result 
ALTHO A 
B. you should eliminate part2 
before troubleshooting in part3 
core:eride~ce 
gcfion:regsozt 
THIS IS C.2 
BECAUSE I 
evidence:core 
c=uae:e.~ect 
C.1 AND 
THUS 
Figure 2: The RDA analysis of the example in Fig- 
ure 1 
categorization of core:contributor relations will not 
be assessed here. 
The reliability coder coded one quarter of the cur- 
rently analyzed corpus, consisting of 132 clauses, 51 
segments, and 70 relations. Here we report the per- 
centage of instances for which the reliability coder 
agreed with the main coder on the various aspects 
of coding. 
There are several kinds of judgements made in an 
RDA analysis, and all of them are possible sources 
of disagreement. First, the two coders could analyze 
a contributor as supporting different cores. This oc- 
curred 7 times (90% agreement). Second, the coders 
could disagree on the core of a segment. This oc- 
curred 2 times (97% agreement). Third, the coders 
could disagree on which relation a cue was associ- 
ated with. This occurred 1 time (98% agreement). 
The final source of disagreement reflects more of a 
theoretical question than a question of reliable anal- 
ysis. The coders could disagree on whether a rela- 
turn should be further analyzed into an embedded 
core:contributor structure. This occurred 8 times 
(91% agreement). 
These rates of agreement cannot be sensibly com- 
pared to those found in studies of (nonembedded) 
segmentation agreement (Grosz and Hirschberg, 
1992; Passonneau and Litman, 1993; Hearst, 1994) 
because our assessment of RDA reliability differs 
from this work in several key ways. First, the RDA 
coding task is more complex than identifying lo- 
cations of segment boundaries. Second, our sub- 
jects/coders are not naive about their task; they are 
trained. Finally, the data is not spoken as in these 
other studies. 
Future work will include a more extensive relia- 
bility study, one that includes the intentional and 
informational relations. 
132 
3 Initial results and their application 
For each tutor explanation in our corpus, each coder 
analyzes the text as described above, and then en- 
ters this analysis into a database. The technique 
of representing an analysis in a database and then 
using database queries to test hypotheses is similar 
to work using RST analyses to investigate the form 
of purpose clauses (Vander Linden et al., 1992). Be- 
cause our analysis is exhaustive, information about 
both occurrence and nonoccurrence of cues can be 
retrieved from the database in order to test and mod- 
ify hypotheses about cue usage. That is, both cue- 
based and factor-based retrievals are possible. In 
cue-based retrievals, we use an occurrence of the cue 
under investigation as the criterion for retrieving the 
value of its hypothesized descriptive factors. Factor- 
based retrievals provide information about cues that 
is unique to this study. In factor-based retrieval, 
the occurrence of a combination of descriptive factor 
values is the criteria for retrieving the accompanying 
cues. In this section, we report two results, one from 
each perspective: a comparison of the distribution of 
sn~cE and BECAUSE in our corpus, and the impact of 
embeddedness on cue selection. 
These results are based on the portion of our cor- 
pus that is analyzed and entered into the database, 
approximately 528 clauses. These clauses comprise 
216 segments in which 287 relations were analyzed. 
Accompanying these relations were 165 cue occur- 
rences, resulting from 39 distinct cues. 
3.1 Choice of"Since ~' or "Because" 
SINCE and BECAUSE were two of the most fre- 
quently used cues in our corpus, occurring 23 
and 13 times, respectively. To investigate their 
distribution, we began with the proposal of 
Elhadad and McKeown (1990). As with our study, 
their work aims to define each cue in terms of fea- 
tures of the propositions it connects for the pur- 
pose of cue selection during text generation. Their 
work relies on the literature and intuitions to identify 
these features, and thus provides an important back- 
ground for a corpus study by suggesting features to 
include in the corpus analysis and initial hypotheses 
to investigate. 
Quirk et al. (1972) note several distributional dif- 
ferences between the two cues: (i) since is used when 
the contributor precedes the core, whereas BECAUSE 
typically occurs when the core precedes the contribu- 
tor, (ii) BECAUSE can be used to directly answer a ~#hy 
question, whereas SINCE cannot, and (iii) BECAUSE 
can be in the focus position of an it-cleft, whereas 
SINCE cannot. These distributional differences are 
reflected in our corpus, and the ordering difference 
(i) is of particular interest. SINCE and BECAUSE are al- 
ways placed with a contributor. All but one (22/23) 
occurrences of Sn~CE accompanied relations in con- 
tributor:core order, while all (13/13) occurrences of 
BECAUSE accompanied relations in core:contributor 
order 2. 
The crucial factor in distinguishing between S~CE 
and BECAUSE is the relative order of core and contrib- 
utor. Elhadad and McKeown (1990) claim that the 
two cues differ with respect to what Ducrot (1983) 
calls "polyphony", i.e., whether the subordinate re- 
latum is attributed to the hearer or to the speaker. 
The idea is that SINCE is used when a relatum has 
its informational source with the hearer (e.g., by 
being previously said or otherwise conveyed by the 
hearer). BECAUSE is monophonous, i.e., its relata 
originate from a single utterer, while sINCE can be 
polyphonous. According to Elhadad and McKeown, 
polyphony is a kind of given-new distinction and 
thus the ordering difference between the two cues 
reduces to the well-known tendency for given to pre- 
cede new. Unfortunately, this characterization of 
the distinction between s~cg and BECAUSE is not 
supported by our corpus study. 
As shown in Figure 3, whether or not contribu- 
tors could be attributed to the hearer did not corre- 
late with the choice of SINCE or BECAUSE. To judge 
whether a contributor is attributable to the student, 
mention of ~n action or result of a test that the 
student previously performed (e.g., you tested 30 to 
9round earlier) was counted as 'yes', while informa- 
tion available by observation (e.g., partl a~d part2 
are co~r~ected b~l wires), specialized circuit knowl- 
edge (e.g., part1 is used bll this test step) and gen- 
eral knowledge (e.g., part~ is more prone to damage ) 
were counted as 'no'. 
Is contributor Cue choice 
attributable sINCE BECAUSE 
to student? 
yes 13 
no 10 
Figure 3: Polyphony does not underlie the choice 
between SINCE and BECAUSE. 
This result shows that the choice between since 
and BECAUSE is determined by something other than 
the attributability of contributor to hearer. In fu- 
ture work, we will consider other factors that may 
determine ordering as possible alternative accounts 
for this choice. Another factor to be considered in 
distinguishing the two cues is the embeddedness dis- 
cussed in the next section. Furthermore, this result 
demonstrates the need to move beyond small num- 
bers of constructed examples and intuitions formed 
~This included answers that begin with BECAUSE. In 
these cases, we took the core to be the presupposition to 
the question. 
133 
from unsystematic analyses of naturally occurring 
data. Only by an exhaustive analysis such as ours 
can hypotheses such as the one discussed here be 
systematically evaluated. 
3.2 Effect of Segment Embeddedness on 
Cue Selection 
The second question we report on here concerns 
whether segment embeddedness affects cue selection. 
Much of the work on cue usage, e.g., (Elhadad and 
McKeown, 1990; Millis etal., 1993; Schiffrin, 1987; 
Zukerman, 1990) has focused on pairs of text spans, 
and this has led to the development of heuristics 
for cue selection that take into account the relation 
between the spans and other local features of the two 
relata (e.g., relative ordering of core and contributor, 
complexity of each span). However, analysis of our 
corpus led us to hypothesize that the hierarchical 
context in which a relation occurs, i.e., what seg- 
ment(s) the relation is embedded in, is a factor in 
cue usage. 
For example, recall that the relation between C.1 
and C.2 in Figure 2 was expressed as part~ is moved 
frequently, AND THUS it is more susceptible to dam- 
age. Now, the relation between C.1 and C.2 could 
have been expressed, BECAUSE part2 is muted fre- 
quently, it is more musceptible to damage. However, 
this relation is embedded in the contributor of the 
relation between B and C, which is cued by THIS IS 
BECAUSE. Intuitively, we expect that, when a rela- 
tion is embedded in another relation already marked 
by BECAUSE, a speaker will select an alternative to 
BECAUSE to mark the embedded relation. That is, 
two relations, one embedded in the other, should be 
signaled by different cues. Because RDA analyses 
capture the hierarchical structure of texts, we were 
able to explore the effect of embedding on cue selec- 
tion. 
We hypothesized that cue selection for one rela- 
tion constrains the cue selection for relations em- 
bedded in it to be a different cue. To test this hy- 
pothesis, we paired each cue occurrence with all the 
other cue occurrences in the same turn. Then, for 
each pair of cues in the same turn, it was catego- 
rized in two ways: (1) the embeddedness of the rela- 
tions associated with the two cues, and (2) whether 
the two cues are the same, alternatives or different. 
Two cues are alternatives when their use with a re- 
lation would contribute (approximately) the same 
semantic content s . The sets of alternatives in our 
data are {ALSO,AND}, {BUT,ALTHOUGH,HOWEVER) and 
SBecause it is based on a test of intersubstitutability, 
the taxonomy proposed by Knott and Dale (1994) does 
not establish the sets of alternatives that are of inter- 
est here. Two cues may be intersubstitutable in some 
contexts but not semantic alternatives (e.g., AND and 
BECAUSE), or they may be semantic alternatives but not 
intersubstitutable because they are placed in different 
positions in a relation (e.g., so and BECAUSE). 
{BECAUSE,SINCE,SO,THUS,THEREFOI:tE}. The question 
is whether the choice between the same and an al- 
ternate cue correlates with the embeddedness of the 
two relations. 
As shown in Figure 4, we can conclude that, when 
a relation is going to have a cue that is semantically 
similar to the cue of a relation it is embedded in, an 
alternative cue must be chosen. Other researchers in 
text generation recognized the need to avoid repeti- 
tion of cues within a single text and devised heuris- 
tics such as "avoid repeating the same connective 
as long as there are others available" (Roesner and 
Stede, 1992). Our results show that this heuristic 
is over constraining. The first column of Figure 4 
shows that the same cue may occur within a single 
explanation as long as there is no embedding be- 
tween the two relations being cued. Based on these 
results, our text generation algorithm will use em- 
beddedness as a factor in cue selection. 
Are relat|ons II Cue choice 
embedded? Same I Alternate 
. .. yes 0 7 
no 6 18 
Figure 4: Embeddedness correlates with choice be- 
tween same and alternate cues. 
4 Conclusions 
We have introduced Relational Discourse Analysis, a 
coding scheme for the exhaustive analysis of text or 
single speaker discourse. RDA is a synthesis of ideas 
from two theories of discourse structure (Grosz and 
Sidner, 1986; Mann and Thompson, 1988). It pro- 
vides a system for analyzing discourse and formulat- 
ing hypotheses about cue selection and placement. 
The corpus study results in rules for cue selection 
and placement that will then be exercised by our 
text generator. Evaluation of these automatically 
generated texts forms the basis for further explo- 
ration of the corpus and subsequent refinement of 
the rules for cue selection and placement. 
Two initial results from the corpus study were 
reported. While the factor of core:contributor or- 
der accounted for the choice between s~ce and BE- 
CAUSE, this factor could not be explained in terms 
of whether the contributor can be attributed to the 
hearer. Alternative explanations for the ordering 
factor will be explored in future work, including 
other types given-new distinctions and larger con- 
textual factors such as focus. Second, the cue selec- 
tion for one relation was found to constrain the cue 
selection for embedded relations to be distinct cues. 
Both of these results are being implemented in our 
text generator. 
134 
Acknowledgments 
The research described in this paper was supported 
by the Office of Naval Research, Cognitive and Neu- 
ral Sciences Division (Grant Number: N00014-91-J- 
1694), and a grant from the DoD FY93 Augmen- 
tation of Awards for Science and Engineering Re- 
search Training (ASSERT) Program (Grant Num- 
ber: N00014-93-I-0812). We are grateful to Erin 
Glendening for her patient and careful coding and 
database entry, and to Maria Gordin for her relia- 
bility coding. 

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