EVALUATING DISCOURSE PROCESSING ALGORITHMS 
Marilyn A. Walker 
Hewlett Packard Laboratories 
Filton Rd., Bristol, England B$12 6QZ, U.K. 
& University of Pennsylvania 
lyn%lwalker~hplb.hpl.hp.com 
Abstract 
In order to take steps towards establishing a method- 
ology for evaluating Natural Language systems, we 
conducted a case study. We attempt to evaluate two 
different approaches to anaphoric processing in dis- 
course by comparing the accuracy and coverage of 
two published algorithms for finding the co-specifiers 
of pronouns in naturally occurring texts and dia- 
logues. We present the quantitative results of hand- 
simulating these algorithms, but this analysis natu- 
rally gives rise to both a qualitative evaluation and 
recommendations for performing such evaluations in 
general. We illustrate the general difficulties encoun- 
tered with quantitative evaluation. These are prob- 
lems with: (a) allowing for underlying assumptions, 
(b) determining how to handle underspecifications, 
and (c) evaluating the contribution of false positives 
and error chaining. 
1 Introduction 
In the course of developing natural language inter- 
faces, computational linguists are often in the posi- 
tion of evaluating different theoretical approaches to 
the analysis of natural language (NL). They might 
want to (a) evaluate and improve on a current sys- 
tem, (b) add a capability to a system that it didn't 
previously have, (c) combine modules from different 
systems. 
Consider the goal of adding a discourse compo- 
nent to a system, or evaluating and improving one 
that is already in place. A discourse module might 
combine theories on, e.g., centering or local focus- 
ing \[GJW83, Sid79\], global focus \[Gro77\], coher- 
ence relations\[Hob85\], event" reference \[Web86\], in- 
tonational structure \[PH87\], system vs. user be- 
liefs \[Po186\], plan or intent recognition or production 
\[(3o578, AP86, SIS1\], control\[WSSS\], or complex syn- 
tactic structures \[Pri85\]. How might one evaluate the 
relative contributions of each of these factors or com- 
pare two approaches to the same problem? 
In order to take steps towards establishing a 
methodology for doing this type of comparison, we 
conducted a case study. We attempt to evalu- 
ate two different approaches to anaphoric processing 
in discourse by comparing the accuracy and cover- 
age of two published algorithms for finding the co- 
specifiers of pronouns in naturally occurring texts and 
dialogues\[Hob76b, BFP87\]. Thus there are two parts 
to this paper: we present the quantitative results of 
hand-simulating these algorithms (henceforth Hobbs 
algorithm and BFP algorithm), but this analysis nat- 
urally gives rise to both a qualitative evaluation and 
recommendations for performing such evaluations in 
general. We illustrate the general difficulties encoun- 
tered with quantitative evaluation. These are prob- 
lems with: (a) allowing for underlying assumptions, 
(b) determining how to handle underspecifications, 
and (c) evaluating the contribution of false positives 
and error chaining. 
Although both algorithms are part of theories of 
discourse that posit the interaction of the algorithm 
with an inference or intentional component, we will 
not use reasoning in tandem with the algorithm's op- 
eration. We have made this choice because we want 
to be able to analyse the performance of the algo- 
rithms across different domains. We focus on the 
linguistic basis of these approaches, using only selec- 
tional restrictions, so that our analysis is independent 
of the vagaries of a particular knowledge representa- 
tion. Thus what we are evaluating is the extent to 
which these algorithms suffice to narrow the search 
of an inference component I. This analysis gives us 
l But note the definition of success in section 2.1. 
251 
some indication of the contribution of syntactic con- 
straints, task structure and global focus to anaphoric 
processing. 
The data on which we compare the algorithms are 
important if we are to evaluate claims of general- 
ity. If we look at types of NL input, one clear di- 
vision is between textual and interactive input. A 
related, though not identical factor is whether the 
language being analysed is produced by more than 
one person, although this distinction may be con- 
fluted in textual material such as novels that contain 
reported conversations. Within two-person interac- 
tive dialogues, there are the task-oriented master- 
slave type, where all the expertise and hence much 
of the initiative, rests with one person. In other two- 
person dialogues, both parties may contribute dis- 
course entities to the conversation on a more equal 
basis. Other factors of interest are whether the di- 
alogues are human-to-human or human-to-computer, 
as well as the modality of communication, e.g. spoken 
or typed, since some researchers have indicated that 
dialogues, and particularly uses of reference within 
them, vary along these dimensions \[Coh84, Tho80, 
GSBC86, D J89, WS89\]. 
We analyse the performance of the algorithms on 
three types of data. Two of the samples are those that 
Hobbs used when developing his algorithm. One is an 
excerpt from a novel and the other a sample of jour- 
nalistic writing. The remaining sample is a set of 5 
human-human, keyboard-mediated, task-oriented di- 
alogues about the assembly of a plastic water pump 
\[Coh84\]. This covers only a subset of the above types. 
Obviously it would be instructive to conduct a similar 
analysis on other textual types. 
2 Quantitative 
Evaluati0n-Black Box 
2.1 The Algorithms 
When embarking on such a comparison, it would be 
convenient to assume that the inputs to the algo- 
rithms are identical and compare their outputs. Un- 
fortunately since researchers do not even agree on 
which phenomena can be explained syntactically and 
which semantically, the boundaries between two mod- 
ules are rarely the same in NL systems. In this case 
the BFP centering algorithm and Hobbs algorithm 
both make ASSUMPTIONS about other system com- 
ponents. These are, in some sense, a further specifi- 
cation of the operation of tile algorithms that must 
be made in order to hand-simulate the algorithms. 
There are two major sets of assumptions, based on 
discourse segmentation and syntactic representation. 
We attempt to make these explicit for each algorithm 
and pinpoint where the algorithms might behave dif- 
ferently were these assumptions not well-founded. 
In addition, there may be a number of UNDER- 
SPECIFICATIONS in the descriptions of the algorithms. 
These often arise because theories that attempt to 
categorize naturally occurring data and algorithms 
based On them will always be prey to previously un- 
encountered examples. For example, since the BFP 
salience hierarchy for discourse entities is based on 
grammatical relation, an implicit assumption is that 
an utterance only has one subject. However the novel 
Wheels has many examples of reported dialogue such 
as She continued, unperturbed, ~Mr. Vale quotes 
the Bible about air pollution." One might wonder 
whether the subject is She or Mr. Vale. In some 
cases, the algorithm might need to be further speci- 
ficied in order to be able to process any of the data, 
whereas in others they may just highlight where the 
algorithm needs to be modified (see section 3.2). In 
general we count underspecifications as failures. 
Finally, it may not be clear what the DEFINITION 
OF SUCCESS is. In particular it is not clear what to 
do in those cases where an algorithm produces multi- 
ple or partial interpretations. In this situation a sys- 
tem might flag the utterance as ambiguous and draw 
in support from other discourse components. This 
arises in the present analysis for two reasons: (1) the 
constraints given by \[GJW86\] do not always allow 
one to choose a preferred interpretation, (2) the BFP 
algorithm proposes equally ranked interpretations in 
parallel. This doesn't happen with the Robbs algo- 
rithm because it proposes interpretations in a sequen- 
tial manner, one at a time. We chose to count as a 
failure those situations in which the BFP algorithm 
only reduces the number of possible interpretations, 
but Robbs algorithm stops with a correct interpre- 
tation. This ignores the fact that tIobbs may have 
rejected a number of interpretations before stopping. 
We also have not needed to make a decision on how to 
score an algorithm that only finds one interpretation 
for an utterance that humans find ambiguous. 
2.1.1 Centering algorithm 
The centering algorithm as defined by Brennan, 
Friedman and Pollard, (BFP algorithm), is derived 
from a set of rules and constraints put forth by Grosz, 
252 
Joshi and Weinstein \[GJW83, GJW86\]. We shall not 
reproduce this algorithm here (See \[BFP87\]). There 
are two main structures in the centering algorithm, 
the CB, the BACKWARD LOOKING CENTER, which is 
what the discourse is 'about', and an ordered list, 
CF, of FORWARD LOOKING CENTERS, which are the 
discourse entities available to the next utterance for 
pronorninalization. The centering framework predicts 
that in a local coherent stretch of dialogue, speakers 
will prefer to CONTINUE talking about the same dis- 
course entity, that the CB will be the highest ranked 
entity of the previous utterance's forward centers that 
is realized in the current utterance, and that if any- 
thing is pronominalized the CB must be. 
In the centering framework, the order of the 
forward-centers list is intended to reflect the salience 
of discourse entities. The BFP algorithm orders this 
list bY grammatical relation of the complements of 
the main verb, i.e. first the subject, then object, 
then indirect object, then other subcategorized-for 
complements, then noun phrases found in adjunct 
clauses. This captures the intuition that subjects are 
more salient than other discourse entities. 
The BFP algorithm added linguistic constraints 
on CONTRA-INDEXING to the centering framework. 
These constraints are exemplified by the fact that, 
in the sentence he Hkes him, the entity cospecified by 
he cannot be the same as that cospecified by him. We 
say that he and him are CONTRA-INDEXED. The BFP 
algorithm depends on semantic processing to precom- 
pute these constraints, since they are derived from 
the syntactic structure, and depend on some notion 
of c-command\[Rei76\]. The other assumption that is 
dependent on syntax is that the the representations 
of discourse entities can be marked with the gram- 
matical function through which they were realized, 
e.g. subject. 
The BFP algorithm assumes that some other mech~ 
anism can structure both written texts and task- 
oriented dialogues into hierarchical segments. The 
present concern is not with whether there might be 
a grammar of discourse that determines this struc- 
ture, or whether it is derived from the cues that 
cooperative speakers give hearers to aid in process- 
ing. Since centering is a local phenomenon and is 
intended to operate within a segment, we needed to 
deduce a segmental structure in order to analyse the 
data. Speaker's intentions, task structure, cue words 
like O.K. now.., intonational properties of utterances, 
coherence relations, the scoping of modal, operators, 
and mechanisms for shift'ing control between dis- 
course participants have all been proposed as ways 
of determining discourse segmentation \[Gro77, GS86, 
Rei85, PH87, HL87, Hob78, Hob85, Rob88, WS88\]. 
Here, we use a combination of orthography, anaphora 
distribution, cue words and task structure. The rules 
are" 
• In published texts, a paragraph is a new seg- 
ment unless the first sentence has a pronoun in 
subject position or a pronoun where none of the 
preceding sentence-internal noun phrases match 
its syntactic features. 
• In the task-oriented dialogues, the action PICK- 
UP marks task boundaries hence segment bound- 
aries. Cue words like nezt, then, and now also 
mark segment boundaries. These will usually co- 
occur but either one is sufficient for marking a 
segment boundary. 
BFP never state that cospecifiers for pronouns 
within the same segment are preferred over those in 
previous segments, but this is an implicit assump- 
tion, since this line of research is derived from Sid- 
ner's work on local focusing. Segment initial utter- 
ances therefore are the only situation where the BFP 
algorithm will prefer a within-sentence noun phrase 
as the cospecifier of a pronoun. 
2.1.2 Hobbs ~ algorithm 
The Hobbs algorithm is based on searching for a 
pronoun's co-specifier in the syntactic parse tree of 
input sentences \[Hob76b\]. We reproduce this algo- 
rithm in full in the appendix along with an example. 
Hobbs algorithm operates on one sentence at a time, 
but the structure of previous sentences in the dis- 
course is available. It is stated in terms of searches 
on parse trees. When looking for an intrasentential 
antecedent, these searches are conducted in a left-to- 
right, breadth-first manner. However, when looking 
for a pronoun's antecedent within a sentence, it will 
go sequentially further and further up the tree to the 
left of the pronoun, and that failing will look in the 
previous sentence. Hobbs does not assume a segmen- 
tation of discourse structure in this algorithm; the 
algorithm will go back arbitrarily far in the text to 
find an antecedent. In more recent work, Hobbs uses 
the notion of COHERENCE RELATIONS to structure the 
discourse \[HM87\]. 
The order by which Hobbs' algorithm traverses the 
parse tree is the closest thing in his framework to pre- 
dictions about which discourse entities are salient. In 
the main it prefers co-specifiers for pronouns that 
253 
are within the same sentence, and also ones that 
are closer to the pronoun in tile sentence. This 
amounts to a claim that different discourse entities 
are salient, depending on the position of a pronoun 
in a sentence. When seeking an intersentential co- 
specification, Hobbs algorithm searches the parse tree 
of the previous utterance breadth-first, from left to 
right. This predicts that entities realized in subject 
position are more salient, since even if an adjunct 
clause linearly precedes the main subject, any noun 
phrases within it will be deeper in the parse tree. This 
also means that objects and indirect objects will be 
among the first possible antecedents found, and in 
general that the depth of syntactic embedding is an 
important determiner of discourse prominence. 
Turning to the assumptions about syntax, we note 
that Hobbs assumes that one can produce the cor- 
rect syntactic structure for an utterance, with all ad- 
junct phrases attached at the proper point of the 
parse tree. In addition, in order to obey linguistic 
constraints on coreference, the algorithm depends on 
the existence of a N parse tree node, which denotes 
a noun phrase without its determiner (See the ex- 
ample in the Appendix). Hobbs algorithm procedu- 
rally encodes contra-indexing constraints by skipping 
over NP nodes whose N node dominates the part of 
the parse tree in which the pronoun is found, which 
means that he cannot guarantee that two contra- 
indexed pronouns will not choose the same NP as 
a co-specifier. 
Hobbs also assumes that his algorithm can some- 
how collect discourse entities mentioned alone into 
sets as co-specifiers of plural anaphors. Hobbs dis- 
cusses at length other assumptions that he makes 
about the capabilities of an interpretive process that 
operates before the algorithm \[Hob76b\]. This in- 
cludes such things as being able to recover syntac- 
tically recoverable omitted text, such as elided verb 
phrases, and the identities of the speakers and hearers 
in a dialogue. 
2.1.3 Summary 
A major component of any discourse algorithm is the 
prediction of which entities are salient, even though 
all the factors that contribute to the salience of a dis- 
course entity have not been identified \[Pri81, Pri85, 
BF83, HTD86\]. So an obvious question is when the 
two algorithms actually make different predictions. 
The main difference is that the choice of a co-specifier 
for a pronoun in the Hobbs algorithm depends in part 
on the position of that pronoun in the sentence. In 
the centering framework, no matter what criteria one 
uses to order the forward-centers list, pronouns take 
the most salient entities as antecedents, irrespective 
of that pronoun's position. Hobbs ordering of enti- 
ties from a previous utterance varies from BFP in 
that possessors come before case-marked objects and 
indirect objects, and there may be some other differ- 
ences as well but none of them were relevant to the 
analysis that follows. 
The effects ot" some of the assumptions are mea- 
surable and we will attempt to specify exactly what 
these effects are, however some are not, e.g. we can- 
not measure the effect of Hobbs' syntax assumption 
since it is difficult to say how likely one is to get the 
wrong parse. We adopt the set collection assumption 
for both algorithms as well as the ability to recover 
the identity of speakers and hearers in dialogue. 
2.2 Quantitative Results of the Algo- 
rithms 
The texts on which the algorithms are analysed are 
the first chapter of Arthur Hailey's novel Wheels, and 
the July 7, 1975 edition of Newsweek. The sentences 
in Wheels are short and simple with long sequences 
consisting of reported conversation, so it is similar to 
a conversational text. The articles from Newsweek 
are typical of journalistic writing. For each text, 
the first 100 occurrences of singular and plural third- 
person pronouns were used to test the performance of 
the algorithms. The task-dialogues contain a total of 
81 uses of it and no other pronouns except for I and 
you. In the figures below note that possessives like 
h/a are counted along with he and that accusatives 
like him and her are counted as he and she 2. 
Wheels 
Newsweek 
Tasks 
N Hobbs 
100 .88 
100 89 
81 51 
BFP 
90 
79 
49 
Figure I: Number correct for both algorithms for 
Wheels, Newsweek and Task Dialogues 
We performed three analyses on the quantitative 
results. A comparison of the two algorithms on each 
data set individually and an overall analysis on the 
three data sets combined revealed no significant dig 
ferences in the performance of the two algorithms 
2Hobbe reports his Mgoritlun's performance and the exam- 
plea it fails on in \[Hob76b, Hob76a\]. The numbers reported 
here vary slightly from those. This is probably due to a dis- 
crepancy in exactly what the data.set consisted of. 
254 
(X 2 = 3.25, not significant). In addition for each 
algorithm alone we tested whether there were signif- 
icant differences in performance for different textual 
types. Both of the algorithms performed significantly 
worse on the task dialogues (X 2 = 22.05 for Hobbs, 
X 2 = 21.55 for BFP, p < 0.05). 
We might wonder with what confidence we should 
view these numbers. A significant factor that must 
be considered is the contribution of FALSE POSITIVES 
and ERROR CHAINING. A FALSE POSITIVE is when 
an algorithm gets the right answer for the wrong rea- 
son. A very simple example of this phenomena is 
illustrated by this sequence from one of the task dia- 
logues. 
Expl: Now put IT in the pan of water. 
Exp2: Stand IT up. 
Exps: Pump the little handle with the red cap 
on IT. 
Clil. ok 
Exp4. Does IT work?? 
The first it in Expl refers to the pump. Hobbs 
algorithm gets the right antecedent for it in Exp3, 
which is the little handle, but then fails on it in Exp4, 
whereas the BFP algorithm has the pump centered at 
Expl and continues to select that as the antecedent 
for it throughout the text. This means BFP gets the 
wrong co-specifier in Exps but this error allows it to 
get the correct co-specifier in Exp4. 
Another type of false positive example is "Every- 
body and HIS brother suddenly wants to be the Presi- 
dent's friend, n said one aide. Hobbs gets this correct 
as long as one is willing to accept that Everybody is 
really the antecedent of his. It seems to me that this 
might be an idiomatic use. 
ERROR CHAINING refers to the fact that once an al- 
gorithm makes an error, other errors can result. Con- 
sider: 
Cli1: Sorry no luck. 
Expx: I bet IT's the stupid red thing. 
Exp2: Take IT out. 
Cli2: Ok. IT is stuck. 
In this example once an algorithm fails at Expx it 
will fail on Exp2 and Cli2 as well since the choices of 
a cospeciller in the following examples are dependent 
on the choice in Expl. 
It isn't possible to measure the effect of false pos- 
itives, since in some sense they are subjective judge- 
ments. However one can and should measure the ef- 
fects of error chaining, since reporting numbers that 
correct for error chaining is misleading, but if the er- 
ror that produced the error chain can be corrected 
then the algorithm might show a significant improve- 
ment. In this analysis, error chains contributed 22 
failures to Hobbs' algorithm and 19 failures to BFP. 
3 Qualitative 
Evaluation-Glass Box 
The numbers presented in the previous section are 
intuitively unsatisfying. They tell us nothing about 
what makes the algorithms more or less general, or 
how they might be improved. In addition, given the 
assumptions that we needed to make in order to pro- 
duce them, one might wonder to what extent the data 
is a result of these assumptions. Figure 1 also fails to 
indicate whether the two algorithms missed the same 
examples or are covering a different set of phenomena, 
i.e. what the relative distribution of the successes and 
failures are. But having done the hand-simulation in 
order to produce such numbers, all of this informa- 
tion is available. In this section we will first discuss 
the relative importance of various factors that go into 
producing the numbers above, then discuss if the al- 
gorithms can be modified since the flexibility of a 
framework in allowing one to make modifications is 
an important dimension of evaluation. 
3.1 Distributions 
The figures 2, 3 and 4 show for each pronominal cat- 
egory, the distribution of successes and failures for 
both algorithms. 
HE 
SHE 
THEY 
Total 
Both Neither Hobbs BFP 
only only 
66 1 1 
6 
6 3 3 
5 1 1 
83 5 5 7 
Figure 2: Distribution on Wheels 
Since the main purpose of evaluation must be to 
improve the theory that we are evaluating, the most 
interesting cases are the ones on which the algo- 
rithrns' performance varies and those that neither al- 
gorithm gets correct. We discuss these below. 
255 
HE 
IT 
THEY 
Total 
Both Neither Hobbs BFP 
only only 
53 8 2 
Ii 5 4 I 
13 3 
77 8 12 3 
Figure 3: Distribution on Newsweek 
I Both Neither Hobbs BFP 
only only 
IT 48 29 3 1 
Figure 4: Distribution on Task Dialogues 
3.1.1 Both 
In the Wheels data, 4 examples rest on the assump- 
tion that the identities of speakers and hearers is re- 
coverable. For example in The GM president smiled. 
"Except Henry will be damned forceful and the papers 
won't print all HIS language. ~, getting the his correct 
here depends on knowing that it is the GM president 
speaking. Only 4 examples rest on being able to pro- 
duce collections or discourse entities, and 2 of these 
occurred with an explicit instruction to the hearer to 
produce such a collection by using the phrase them 
both. 
3.1.2 Hobbs only 
There are 21 cases that Hobbs gets that BFP don't, 
and of these these a few classes stand out. In ev- 
ery case the relevant factor is Hobbs' preference for 
intrasentential co-specifiers. 
One class, (n = 3), is exemplified by Put the lit- 
tle black ring into the the large blue CAP with the 
hole in IT. All three involved using the preposition 
with in a descriptive adjunct on a noun phrase. It 
may be that with-adjuncts are common in visual de- 
scriptions, since they were only found in our data in 
the task dialogues, and a quick inspection of Grosz's 
task-oriented dialogues revealed some as well\[Deu74\]. 
Another class, (n = 7), are possessives. In some 
cases the possessive co-specified with the subject of 
the sentence, e.g. The SENATE took time from 
ITS paralyzing New Hampshire election debate to 
vote agreement, and in others it was within a rela- 
tive clause and co-specified with the subject of that 
clause, e.g. The auto industry should be able to pro- 
duce a totally safe, defect-free CAR that doesn't pol- 
lute ITS environment. 
Other cases seem to be syntactically marked sub- 
ject matching with constructions that link two S 
clauses (n = 8). These are uses of more-than in e.g. 
but Chamberlain grossed about $8.3 million more than 
HE could have made by selling on the home front. 
There also are S-if-S cases, as in Mondale said: "I 
think THE MAFIA would be broke if'IT conducted all 
its business that way." We also have subject match- 
ing in AS-AS examples as in ... and the resulting EX- 
POSURE to daylight has become as uncomfortable as 
IT was unaccustomed, as well as in sentential com- 
plements, such as But another liberal, Minnesota's 
Walter MONDALE, said HE had found a lot of in- 
competence in the agency's operations. The fact that 
quite a few of these are also marked with But may be 
significant. 
In terms of the possible effects that we noted ear- 
lier, the DEFINITION OF SUCCESS (see section 2.1 fa- 
vors Hobbs (n = 2). Consider: 
K: Next take the red piece that is the small- 
est and insert it into the hole in the side of 
the large plastic tube. IT goes in the hole 
nearest the end with the engravings on IT. 
The Hobbs algorithm will correctly choose the end 
as the antecedent for the second it. The BFP al- 
gorithm on the other hand will get two interpreta- 
tions, one in which the second it co-specifies the red 
piece and one in which it co-specifies the end. They 
are both CONTINUING interpretations since the first 
it co-specifies the CB, but the constraints don't make 
a choice. 
3.1.3 BFP only 
All of the examples on which BFP succeed and Hobbs 
fails have to do with extended discussion of one dis- 
course entity. For instance: 
Expt: Now take the blue cap with the two 
prongs sticking out (CB -- blue cap) 
Exp2: and fit the little piece of pink plastic on IT. 
Ok? (CB= blue cap) 
Clit : ok. 
Exp3: Insert the rubber ring into that blue cap. 
(CB= blue cap) 
Exp4: Now screw IT onto the cylinder. 
On this example, Hobbs fails by choosing the co- 
specifier of it in Exp4 to be the rubber ring, even 
256 
though the whole segment has been about the blue 
cap. 
Another example from the novel WHEELS is given 
below. On this one Hobbs gets the first use of he 
but then misses the next four, as a result of missing 
the second one by choosing a housekeeper as the co- 
specifier for HIS. 
..An executive vice-president of Ford was 
preparing to leave for Detroit Metropoli- 
tan Airport. HE had already breakfasted, 
alone. A housekeeper had brought a tray to 
HIS desk in the softly lighted study where, 
since 5 a.m., HE had been alternately read- 
ing memoranda (mostly on special blue sta- 
tionery which Ford vice-presidents used in 
implementing policy) and dictating crisp in- 
structions into a recording machine. HE had 
scarcely looked up, either as the mall ar- 
rived, or while eating, as HE accomplished 
in an hour what would have taken... 
Since an ezecutive vice-president is centered in the 
first sentence, and continued in each following sen- 
tence, the BFP algorithm will correctly choose the 
cospecifier. 
3.1.4 Neither 
Among the examples that neither algorithm gets cor- 
rectly are 20 examples from the task dialogues of it 
referring to the global focus, the pump. In 15 cases, 
these shifts to global focus are marked syntactically 
with a cue word such as Now, and are not marked 
in 5 cases. Presumably they are felicitous since the 
pump is visually salient. Besides the global focus 
cases, pronominal references to entities that were not 
linguistically introduced are rare. The only other ex- 
ample is an implicit reference to 'the problem' of the 
pump not working: 
Clil: Sorry no luck. 
Expl: I bet IT's the stupid red thing. 
We have only two examples of sentential or VP 
anaphora altogether, such as Madam Chairwoman, 
said Colby at last, I am trying to ran a secret intelli- 
gence service. IT u~as a forlorn hope. Neither Hobbs 
algorithm nor BFP attempt to cover these examples. 
Three of the examples are uses of it that seem to 
be lexicalized with certain verbs, e.g. They hit IT 
off real well. One can imagine these being treated as 
phrasal lexical items, and therefore not handled by 
an anaphoric processing component\[AS89\]. 
Most of the interchanges in the task dialogues con- 
sist of the client responding to cotmnands with cues 
such as O.K. or Ready to let the expert know when 
they have completed a task. When both parties 
contribute discourse entities to the common ground, 
both algorithms may fail (n = 4). 
Consider: 
Expl: Now we have a little red piece left 
Exp2: and I don't know what to do with IT. 
Clil: Well, there is a hole in the green plunger 
inside the cylinder. 
Expa: I don't think IT goes in THERE. 
Exp4: I think IT may belong in the blue cap 
onto which you put the pink piece 
of plastic. 
In Exp3, one might claim that it and there are con- 
traindexed, and that there can be properly resolved 
to a hole, so that it cannot be any of the noun phrases 
in the prepositional phrases that modify a hole, but 
whether any theory of contra-indexing actually give. 
us this is questionable. 
The main factor seems to be that even though 
Expt is not syntactically a question, the little red 
piece is the focus of a question, and as such is in 
focus despite the fact that the syntactic construction 
there is supposedly focuses a hole in the green plunger 
...\[Sid79\]. These examples suggest that a questioned 
entity is left focused until the point in the dialogue at 
which the question is resolved. The fact that well has 
been noted as a marker of response to questions sup- 
ports this analysis\[Sch87\]. Thus the relevant factor 
here may be the switching of control among discourse 
participants \[WS88\]. These mixed-initiati.ve features 
make these sequences inherently different than text. 
3.2 Modifiability 
Task structure in the pump dialogues is an important 
factor especially as it relates to the use of global focus. 
Twenty of the cases on which both algorithms fail are 
references to the pump, which is the global focus. We 
can include a global focus in the centering framework, 
as a separate notion from the current CB. This means 
that in the 15 out of 20 cases where the shift to global 
focus is identifiably marked with a cue-word such as 
now, the segment rules will allow BFP to get the 
global focus examples. 
BFP can add the VP and the S onto the end of the 
257 
forward centers list, as Sidner does in her algorithm 
for local focusing \[Sid79\]. This lets BFP get the two 
examples of event anaphora. Hobbs discusses the fact 
that his algorithm cannot be modified to get event 
anaphora in \[Hob76b\]. 
Another interesting fact is that in every case in 
which Hobbs' algorithm gets the correct co-specifier 
and BFP didn't, the relevant factor is Hobbs' pref- 
erence for intrasentential co-specifiers. One view 
on these cases may be that these are not discourse 
anaphora, but there seems to be no principled way 
to make this distinction. However, Carter has pro- 
posed some extensions to Sidner's algorithm for lo- 
cal focusing that seem to be relevant here(chap. 6, 
\[Car87\]). He argues that intra-sentential candidates 
(ISCs) should be preferred over candidates from the 
previous utterance, ONLY in the cases where no dis- 
course center has been established or the discourse 
center is rejected for syntactic or selectional reasons. 
He then uses Hobbs algorithm to produce an ordering 
of these ISCs. This is compatible with the centering 
framework since it is underspecifled as to whether one 
should always choose to establish a discourse center 
with a co-specifier from a previous utterance. If we 
adopt Carter's rule into the centering framework, we 
find that of the 21 cases that Hobbs gets that BFP 
don't, in 7 cases there is no discourse center estab- 
lished, and in another 4 the current center can be re- 
jected on the basis of syntactic or sortal information. 
Of these Carter's rule clearly gets 5, and another 3 
seem to rest on whether one might want to establish 
a discourse entity from a previous utterance. Since 
the addition of this constraint does not allow BFP to 
get any examples that neither algorithm got, it seems 
that this combination is a way of making the best out 
of both algorithms. 
The addition of these modifications changes the 
quantitative results. See the Figure 5. 
N 
Wheels 100 
Newsweek 100 
Tasks 81 
Hobbs BFP 
88 93 
89 84 
51 64 
Figure 5: Number correct for both algorithms after 
Modifications, for Wheels, Newsweek and Task Dia- 
logues 
However, the statistical analyses still show that 
there is no significant difference in the performance 
of the algorithms in general. It is also still the case 
that the performance of each algorithm significantly 
varies depending on tile data. Tile only significant 
difference as a result of the modifcations is that tile 
BFP algorithm now performs significantly better oil 
tile pump dialogues alone (X 2 = 4.3 I, p < .05). 
4 Conclusion 
We can benefit in two ways from performing such 
evaluations: (a) we get general results on a methodol- 
ogy for doing evaluation, (b) we discover ways we can 
improve current theories. A split of evaluation efforts 
into quantitative versus qualitative is incoherent. We 
cannot trust the results of a quantitative evaluation 
without doing a considerable amount of qualitative 
analyses and we should perform our qualitative anal- 
yses on those components that make a significant con- 
tribution to the quantitative results; we need to be 
able to measure the effect of various factors. These 
measurements must be made by doing comparisons 
at the data level. 
In terms of general results, we have identified some 
factors that make evaluations of this type more com- 
plicated and which might lead us to evaluate solely 
quantitative results with care. These are: (a) To de- 
cide how to evaluate UNDERSPECIFICATIONS and the 
contribution of ASSUMPTIONS, and (b) To determine 
the effects of FALSE POSITIVES and ERKOR CHAINING. 
We advocate an approach in which the contribution 
of each underspeeification and assumption is tabu- 
lated as well as the effect of error chains. If a prin- 
cipled way could be found to identify false positives, 
their effect should be reported as well as part of any 
quantitative evaluation. 
In addition, we have takeri a few steps towards de- 
termining the relative importance of different factors 
to the successful operation of discourse modules. The 
percent of successes that both algorithms get indi- 
cates that syntax has a strong influence, and that at 
the very least we can reduce the amount of inference 
required. In 590£ to 82% of the cases both algorithms 
get the correct result. This probably means that in a 
large number of cases there was no potential conflict 
of co-specifiers. In addition, this analysis has shown, 
that at least for task-oriented dialogues global focus 
is a significant factor, and in general discourse struc- 
ture is more important in the task dialogues. How- 
ever simple devices such as cue words may go a long 
way toward determining this structure. 
Finally, we should note that doing evaluations such 
as this allows us to determine the GENERALITY of our 
258 
approaches. Since the performance of both Hobbs 
and BFP varies according to the type of the text, and 
in fact was significantly worse on the task dialogues 
than on the texts, we might question how their per- 
formance would vary on other inputs. An annotated 
corpus comprising some of the various NL input types 
such as those I discussed in the introduction would 
go a long way towards giving us a basis against which- 
we could evaluate the generality of our theories. 
5 Acknowledgements 
David Carter, Phil Cohen, Nick Haddock, Jerry 
Hobbs, Aravind Joshi, Don Knuth, Candy Sidner, 
Phil Stenton, Bonnie Webber, and Steve Whittaker 
have provided valuable insights toward this endeavor 
and critical comments on a multiplicity of earlier ver- 
sions of this paper. Steve Whittaker advised me on 
the statistical analyses. I would like to thank Jerry 
Hobbs for encouraging me to do this in the first place. 
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260 
A The Hobbs algorithm 
The algorithm and an example is reproduced below. 
In it, NP denotes NOUN PHRASE and S denotes SEN- 
TENCE. 
1. Begin at the NP node immediately dominating 
the pronoun in the parse tree of S. 
2. Go up the tree until you encounter an NP or S 
node. Call this node X, and call the path used 
to reach it p. 
3. Traverse all branches below node X to the left 
of path p in a left-to-right breadth-first fashion. 
Propose as the antecedent any NP node encoun- 
tered that has an NP or S node on the path from 
it to X. 
4. If X is not the highest S node in the sentence, 
continue to step 5. Otherwise traverse the sur- 
face parse trees of previous sentences in the text 
in reverse chronological order until an acceptable 
antecedent is found; each tree is traversed in a 
left-to-right, breadth-first manner, and when an 
NP node is encountered, it is proposed as the 
antecedent. 
5. From node X, go up the tree to the first NP or 
S node encountered. Call this new node X, and 
call the path traversed to reach it p. 
6. If X is an NP node and if the path p to X did 
not pass through the N node that X immediately 
dominates, propose X as the antecedent. 
7. Traverse all branches below node X to the left 
of path p in a left-to-right, breadth-first manner, 
but do not go below any NP or S node encoun- 
tered. Propose any NP or S node encountered 
as the antecedent. 
8. Go to step 4. 
The purpose of steps 2 and 3 is to observe the 
contra.indexing constraints. Let us consider a sim- 
ple conversational sequence. 
UI: Lyn's morn is a gardener. 
U2: Craige likes her. 
We are trying to find the antecedent for her in the 
second utterance. Let us go through the algorithm 
step by step, using the parse trees for UI and U2 in 
the figure. 
1. NPs labels the starting point of step 1. 
/ 
NP2 
I 
Lyn 
Sl / \ 
NPt VP 
/ \ I 
Det N V 
\ I I 
's room is 
\ 
NP 
I 
Det 
I a 
\ 
N3 \ 
N 
l 
gardener 
S2 /q: 
NP4 VP " 
I / "<'-. 
Craige V NPs 
I I 
likes her 
Figure 6: Parse Trees for Ut and U2 
. 
. 
. 
$2 is called X. We mark the path p with a dotted 
line. 
We traverse S~ to the left of p. We encounter 
NP4 but it does not have an NP or S node be- 
tween it and X. This means that NP4 is contra- 
indexed with NPs. Note that if the structure 
corresponded to Craige"s morn likes her then the 
NP for Craige would be an NP to the left of 
p that has an NP node between it and X, and 
Craige would be selected as the antecedent for 
her. 
The node X is the highest S node in U2, so we 
go to the previous sentence Ut. As we traverse 
the tree of Ut, the first NP we encounter is NP1, 
so Lyn's morn is proposed as the antecedent for 
her and we are done. 
261 
