The Delphi Natural Language Understanding System 
Madeleine Bates, Robert Bobrow, Robert Ingria and David Stallard 
BBN Systems and Technologies, Inc. 
70 Fawcett St. 
Cambridge, MA 02138 
Abstract 
This paper presents Delphi, the natural 
language component of the BBN Spoken 
Language System. Delphi is a domain- 
independent natural language question an- 
swering system that is solidly based on 
linguistic principles, yet which is also ro- 
bust to ungrammatical input. It includes 
a domain-independent, broad-coverage gram- 
mar of English. Analysis components include 
an agenda-based best-first parser and a fall- 
back component for partial understanding that 
works by fragment combination. Delphi has 
been formally evaluated in the ARPA Spoken 
Language program's ATIS (Airline Travel In- 
formation System) domain, and has performed 
well. Delphi has also been ported to a spo- 
ken language demonstration system in an Air 
Force Resource Management domain. We dis- 
cuss results of the evaluation as well as the 
porting process. 
1 Introduction 
Delphi is a natural language understanding system based 
on general linguistic principles which is adaptable to any 
question-answering domain. It incorporates a number of 
domain-independent knowledge bases, including a gen- 
eral, broad-coverage grammar of English with a pow- 
erful and flexible handling of complementation. Unlike 
most other linguistically motivated systems, however, 
Delphi is also highly robust, allowing for partial under- 
standing when an input is ungrammatical, disfluent, or 
not properly transcribed by a speech recognizer. Thus, 
Delphi can be used for a spoken language application 
as readily as for a written one. Furthermore, Delphi's 
partial understanding component, called the Semantic 
Linker, is driven off the same system of semantic rules 
as Delphi's regular best-first parser. Building a robust 
application therefore requires no additional effort. 
There are several components of the system, which 
is diagrammed in Figure 1. First are the parser and 
Semantic Linker, which output an intermediate repre- 
sentation we call a "semantic graph". The semantic 
Z 
t 
I  ..EL L.J 1 
DATABASE \] 
1 RESPONSE 
Figure 1: System Diagram 
graph is passed to a quantification stage which pro- 
duces a fully scoped logical form from it. The logical 
form is then passed to the discourse stage, which re- 
solves pronominal references and performs other types 
of task-dependent constraint resolution to produce the 
final logical form. The final logical form is then passed 
to the backend translator, and then to the application 
system which produces the response. Several knowl- 
edge bases are employed by these analysis components, 
including grammar, "realization rules" and the domain 
model, which represents the set of classes and binary 
relations of the given application domain. 
Delphi differs from most other linguistically moti- 
vated systems in the role that is played by syntax. 
The primary function of Delphi's parser and syntac- 
tic knowledge bases is not to produce a parse tree, but 
rather to constrain the search for an appropriate semantic 
graph interpretation of the utterance. Semantic graphs 
are produced not by rule-to-rule compositionality, but 
by what might be called "relation-to-relation" compo- 
sitionality - the association of grammatical relations in 
the syntactic structure with semantic relations in the se- 
132 
mantic graph. 
This more incremental view of the syntax/semantics 
interface has three crucial advantages. First, there is 
much more flexibility with respect to ordering and op- 
tionality of constituents. Second, because relation-to- 
relation translations are simple, the task of porting the 
system is greatly simplified. Third and finally, partial 
or fragmentary analyses can be represented, and a com- 
plete semantic graph interpretation for the utterance pro- 
duced even when a complete syntactic analyses is not 
available. 
In the remainder of the paper, we describe Del- 
phi's main processing components, representational for- 
malisms, and knowledge bases. 
2 Grammar And The Syntax/Semantics 
Interface 
The Delphi grammar is a broad coverage, domain inde- 
pendent grammar of English written in a version of the 
Definite Clause Grammar formalism (Pereira and War- 
ren, 1980) that has been extended to include labeling of 
right-hand side elements with the grammatical relations 
they bear to the head of the construction. An example 
is: 
(S ?arg ?mood) 
-> 
subject: (NP ?arg ?mood etc.) 
head: (VP ?agr ?mood etc.) 
In this rule, there is a head VP and an NP which 
bears the SUBJECT relation to it. Other grammati- 
cal relations include the familar DIRECT-OBJECT and 
INDIRECT-OBJECT as well as the prepositions, such 
as TO, FROM, WITH and so on. 
Annotating sub-constituents with grammatical rela- 
tions regularizes the syntactic structure with respect to 
particular grammatical rules, and allows a "relation-to- 
relation" form of compositionality, as opposed to the 
more traditional "rule-to-rule" version that is exempli- 
fied by such systems as Gemini (Dowding et al, 1993) 
and the Core Language Engine (Alshawi, 1992). In 
relation-to-relation compositionality, each grammatical 
relation in the syntactic structure corresponds to a se- 
mantic relation in a parallel semantic structure we call 
a "semantic graph". The terminal nodes of the seman- 
tic graph are the word meanings, corresponding to the 
lexical heads of syntactic structure. 
An example of a semantic graph, representing the 
meaning of "What flights fly from Boston to Denver", 
may be seen in Figure 2. The semantic graph is not a 
fully quantified formula; rather it may be thought of as 
a form of predicate-argument representation, with quan- 
tifiers in place, from which a fully quantified formula 
can be generated. The allowed class and relation labels 
come from the domain model. 
This view of the syntax/semantics interface has 
marked advantages. For one thing, because the syn- 
tactic/semantic structure is built up one argument at a 
flight-of 
FLY ~g ~ FLIGHT 
de.st-of 
Figure 2: Semantic Graph 
quant 
~'WH 
time, it becomes much easier to accomodate such phe- 
nomena as order-variation and optionality of arguments 
that are difficult for other approaches 
The importance of this feature may be seen in the 
examples of argument order-variation and optionality 
that abound in real data. Consider the following from 
the ATIS domain, in which complements can vary freely 
in order: 
What flights fly from Boston to Denver? 
What flights fly to Denver from Boston? 
or be separated from the head by a modifier typically 
regarded as an adjunct: 
What flights fly at 3 pm from Boston to Denver? 
In some cases, modifiers can be omitted, as in: 
What flights fly from Boston ? 
What flights fly to Denver? 
and sometimes the omission of an argument can have 
anaphoric consequences, as in: 
What restrictions apply? 
which cannot be felicitously uttered except in a con- 
text where there is something in the discourse that a 
restriction could "apply" to. 
Conventional approaches to subcategorization, such 
as Definite Clause Grammar (Pereira and Warren, 
1980), Categorial Grammar (Ades and Steedman, 1982), 
PATR-II (Shieber, 1986), and lexicalized TAG (Schabes 
et al, 1988) all deal with complementation by includ- 
ing in one form or another a notion of "subcategoriza- 
tion frame" that specifies a sequence of complement 
phrases and constraints on them. Handling all the pos- 
sible variations in complement distribution in such for- 
malisms inevitably leads to an explosion in the number 
of such frames, and a correspondingly more difficult 
task in porting to a new domain. 
In our approach, on the other hand, it becomes pos- 
sible to view subcategorization of a lexical item as a 
set of constraints on the outgoing arcs of its semantic 
graph node. Different types of constraints - order of ar- 
guments, optionality of arguments, semantic-class con- 
straints and semantic effects of arguments - can all be 
represented separately, instead of enumerating all pos- 
sible argument sequences in a set of alternative subcat- 
egorization frames. 
133 
Subcategorization constraints in Delphi are encoded 
in lexical entries using a structure called a "map" (Stal- 
lard and Bobrow, 1991). Below is part of the lexical 
entry for "fly" in the ATIS domain: 
FLY 
subject : FLIGHT-OF 
to : DEST-OF 
from: ORIG-OF 
completion: (and (filled flight-of) 
(or (filled dest-of) 
(filled orig-of) ) 
Map entries have "translation", "realization" and "com- 
pletion" components. The translation part of this entry 
specifies that the lexical head "fly" is to correspond to a 
semantic-graph node labeled with event-class FLY. The 
realization part of the entry specifies what grammati- 
cal relations the lexical item takes, and what semantic 
relations these correspond to, or "realize", in the se- 
mantic graph. Here, the entry specifies that "fly" takes 
SUBJECT, TO, and FROM complements, and that these 
grammatical relations correspond to the semantic rela- 
tions FLIGHT-OF, DEST-OF, and ORIG-OF respec- 
tively. Semantic selectional restrictions in these argu- 
ment positions - that the filler of DEST-OF be a city, 
for example - are implicit from the declarations of the 
relations in the domain model. 
The "completion" part of the entry specifies what out- 
going arcs are required for the node. Here, the entry re- 
quires that the FLIGHT-OF role be filled, and that either 
the DEST-OF or ORIG-OF roles be filled (forbidding 
the intransitive "the flight flies"). More complex op- 
tionality cases are encoded with other completion pred- 
icates. For example, the case where an anaphor must 
be present ("What restrictions apply") is encoded by the 
predicate FILLED-OR-ANAPHOR. 
Some realization rules are tied to semantic classes 
rather than lexical translations, and require for their ap- 
plication only that semantic class restrictions implicit 
from the domain and range of the realized relation be 
satisfied. Typical examples are the rules governing noun 
modifier meanings, such as "Delta flights", "Delta's 
flights", "the flights on/aboard Delta". These would all 
be handled by the global realization rule: 
{NOM-COMP POSS ABOARD ON ...} 
-....} 
AIRLINE-OF 
Determining what semantic relation a given grammat- 
ical relation instance corresponds to is most generally 
viewed as a form of goal-solving in Delphi, in which 
a chain of rules can be invoked. For example, syntac- 
tic constructions such as "X with Y", "X has Y" and 
"X's Y" are interpreted by first appealing to a rule map- 
ping them to a pseudo-relation called GENERALIZED- 
POSSESSION, and then seeking a realization for it that 
is compatible with the classes of X and Y. This avoids 
having to write three different versions of the same re- 
alization rule. 
BOSTON:TO 
DENVER airline-of 
DELTA 
~l~wr 
~ MONDAY:ON 
day-of.week 
Figure 3: Fragment Graphs 
An important advantage of the realization rule for- 
mulation, apart from its its power and flexibility, is its 
simplicity. Realization rules are very simple to write, 
and make maximal use both of knowledge about the 
domain and general knowledge of language. 
3 Ill-Formedness Handling: The 
Semantic Linker 
When an utterance cannot be parsed with Delphi's best- 
first parser (Bobrow, 1991) - either because it is ill- 
formed, mis-recognized by the speech system, or sim- 
ply because it is outside the coverage of the grammar - 
it can still be partially understood by the system, often 
well enough to give the correct response. The compo- 
nent responsible for partial understanding in the Delphi 
system is called the Semantic Linker (Stallard and Bo- 
brow, 1993). 
After a parse fails there is a set of fragmentary con- 
stituents left over in the chart, corresponding to a set of 
semantic graphs. The Semantic Linker seeks to connect 
these sub-graphs into a single connected one by adding 
links between nodes in the different sub-graphs. 
At top-level, this is the same thing that the parser 
and grammar do. The difference is that the parser and 
grammar have an idea of what the grammatical rela- 
tionship between constituents is, based on requirements 
of their proximity in the string and other syntactic ev- 
idence. The Semantic Linker does not have these re- 
quirements, being a looser form of combination that 
can ignore fragment order and skip over intervening, 
unanalyzable material with ease. 
Although it is a very different algorithm, the Seman- 
tic Linker uses the same set of realization rules that 
drives the regular parser. Using the realization rules, 
the Linker determines for each pair of nodes in dif- 
ferent semantic graphs the set of all links which can 
connect them. It then uses an A* search to find the 
most plausible set of links which produce a complete 
graph. 
Suppose for example, we have the three fragments 
"to Boston", "Denver" and "Delta flights on Mon- 
day". Then the three corresponding sub-graphs are 
those shown in Figure 3 where a PP is treated as its 
NP object with the preposition as a tag. For this set of 
fragmentary sub-graphs, the possible links are: 
la. FLIGHTS1--- DEST-OF -> BOSTON:TO 
134 
lb. FLIGHTS1--- ORIG-OF -> BOSTON:TO 
2a. FLIGHTS1--- DEST-OF -> DENVER 
2b. FLIGHTS1--- ORIG-OF -> DENVER 
3a. DENVER--- NEARBY-TO -> BOSTON:TO 
where the links are grouped together in a ordered list 
according to the fragment-pairs they connect. 
The plausibility of a given link is a function of a 
number of different features, including penalities from 
assumptions made in its computation (e.g. that a given 
preposition can be ignored or assumed) and empirically 
determined probabilities for the given link (e.g. that 
given an AIRLINE and a FLIGHT they are most prob- 
ably linked by the relation AIRLINE-OF). 
The semantic linker may also "hallucinate" a new 
node to bridge two fragments between whom no links 
can otherwise be computed. For example, for the utter- 
ance "from Boston to Denver", which has no explicit 
FLIGHT-object, a FLIGHT node can be inserted be- 
tween the fragments to make sense of the utterance. 
Because the Semantic Linker uses the same set of 
realization rules as the rest of the system, when the 
system is ported to a new domain the Semantic Linker 
can be used immediately - a distinct advantage over 
some other approaches to fallback understanding, such 
as (Stallard and Bobrow, 1992) or (Jackson et al, 1991). 
In formal experiments (as we discuss subsequently) 
the Semantic Linker has been show to dramatically im- 
prove Delphi's performance. 
4 Quantification 
The quantifier scoping module in Delphi takes a se- 
mantic graph and produces a fully-scoped expression 
in the logical language FMRL. The basic strategy for 
quantifier scoping is a descendant of that used in the 
LUNAR system (Woods et al, 1978). This is made 
possible by the use of the semantic graph as a com- 
mon underlying representation for both the grammatical 
and ill-formod parts of fragmentary utterances. Delphi's 
scoping module traps quantifiers from relative clauses, 
makes the quantifiers from PPs etc. outscope the NP 
quantifier, and resolves the scope of quantifiers from 
parallel constituents in terms of left-to-right order in 
the input. These general rules are modified to take into 
account differing strengths of quantifiers such as EACH. 
Left-to-right ordering and syntactic structure for 
grammatical portions of the utterance are recovered 
from the semantic graph by backpointers to the lexical 
items and grammatical relations from which the graph 
was produced. Links established by the semantic linker 
are treated by the quantification mechanism as if the 
constituency is indeterminate, so that only left-to-right 
scoping rules and individual quantifier preferences take 
effect. 
The resulting mechanism is robust, and quantifica- 
tional scoping has been an insignificant source of error 
in the official ARPA blind-test evaluations of the ATIS 
system. More complex strategies have been proposed 
and implemented in the last two decades, and could in 
principle be modifed to work with ill-formed input, but 
the simple and robust LUNAR approach handles essen- 
tially all the phenomena seen in the tens of thousands of 
sentences of ATIS training collected during experiments 
with non-linguist users. 
5 Discourse 
The discourse mechanism of Delphi consists of several 
components: resolution of local ambiguities, pronomi- 
nal and deictic antecedent resolution, ellipsis handling 
and discourse constraint propagation. 
The most common case of local ambiguity in the 
ATIS domain involves temporal phrases as in "the nine 
o'clock flight". The resolution mechanism searches 
both for linguistic information in the current and previ- 
ous sentences, as well as properties of entities in previ- 
ous answers, to resolve whether "nine o'clock" is AM 
or PM. 
The pronourddeictic resolution mechanism used in 
Delphi makes use of locally expressed or implied se- 
mantic constraints to search through a set of candidate 
antecedents. The current mechanism ignores syntac- 
tic number as a cue, because empirically in the ATIS 
corpus (and we suspect in other spontaneous speech ap- 
plications) it is often in error. A simple-minded focus 
component is used, primarily based on recency, and sec- 
ondarily based on grammatical relations within an utter- 
ance. Because of the strength of semantic cues and the 
prevalence of ill-formed input, the use of syntactic cues 
for focus is limited. 
The interpretation of later sentences often must in- 
clude information from previous sentences, without ex- 
plicit linguistic cues. This is especially true in "design 
dialogues", where the goal is to find a description of 
a set of objects that will meet some set of implicit or 
explicit constraints. Consider for example the following 
discourse from the ATIS domain. 
Show Delta fights from Boston to Dallas tomorrow. 
Can I leave in the morning? 
Is there a nonstop flight? 
Show me the American flights. 
I want to go from Dallas to Chicago on Wednesday 
Note that the constraints of prior sentences (such as on 
airline, origin, destination etc.) are implicit for subse- 
quent sentences unless contradicted by information in 
the current sentence (e.g. "American" overrides the 
"Delta" from the first sentence) or until there is evidence 
that a new problem is being solved (the new origin and 
destination in the last sentence indicates that all previ- 
ous constraints can be dropped). Delphi has a "context 
tracker" that maintains a stack of the constraints from 
previous utterances, and has a set of rules for when 
constraints are to be modified or deleted before being 
merged with the current sentence. 
Finally, we handle ellipsis as a special case of seman- 
tic linking. If we have the two utterances: 
135 
Show me the meals on the morning flight. 
on American at 12:30 
We can treat these as if they were one run-on ill- 
formed input and link "American" to "flight", and re- 
place "morning" with "12:30", using a minor variant of 
the Semantic Linker linker which allows for later con- 
straints to overwrite earlier ones of the same type. This 
strategy has been very effective, and covers a large class 
of elliptical constructions. 
6 Backend Mapping 
In order to get a response to a user query, the complete 
FMRL interpretation of an utterance must be translated 
to an expression of a target query language which can be 
evaluated directly against the tabular database to retrieve 
the answer. 
A key step is bridging the gap in conceptual vo- 
cabulary between the two representations. For exam- 
ple, the FMRL interpretation of the query "How many 
flights on Delta serve meals" has one-place predicates 
like FLIGHT and AIRLINE, and two-place predicates 
like AIRLINE-OF and MEAL-OF. The database for the 
ATIS domain, on the other hand, only has a single table 
FLIGHT with fields containing airline and meal infor- 
mation. Delphi bridges this gap between representations 
with a system of local mapping rules which translate 
the one- and two-place predicates of the FMRL into ex- 
pressions of a relational algebra target language which 
retrieve the extensions of these predicates. 
Sometimes, however, some combination of FMRL 
predicates has a correspondence in the database but the 
individual predicates themselves do not. For example, 
in the database for the SPLINT domain a table relating 
aircraft-types to their physical characteristics has a field 
for the number of engines the aircraft has, but no rep- 
resentation for the engines themselves. If we now ask 
"How many engines does an F-16 have?", there is no 
local translation of the FMRL predicate ENGINE. 
To deal with this, Delphi has a system of global trans- 
formations that are applied first, rewriting subsets of the 
FMRL clauses to a form that can be handled with local 
translation. The rule that handles this example is: 
(is-a :e engine number) 
(aircraft-engine-of :a :e) 
(is-a *count* number) 
(eq (number-engines-of :a) *count*) 
7 Interface To A Speech Recognizer 
In spoken language applications, Delphi is interfaced 
to the output of the Byblos speech recognition system 
(Bates et al, 1993). The N-best paradigm is used, in 
which the recognizer outputs in order its top N guesses 
at the transcription of the sentence, for some value of N 
(usually 5). Delphi then runs over these transcriptions in 
the order they have been ranked, first with the Semantic 
Linker disabled so that only grammatical utterances are 
136 
allowed, and if none is found, runs over them again 
with the Semantic Linker enabled. 
8 Results Of Formal Evaluation On ATIS 
Our complete system including the Semantic Linker was 
evaluated in the December 1993 ARPA ATIS evalua- 
tion. Prior to evaluation, ATIS versions of the system's 
domain model, lexicon and realization rules had been 
developed using several thousand utterances of training 
data collected from users of ATIS. An approximately 
1000-utterance set was held aside as a blind test set on 
which all participating sites were evaluated. 
Error rate in this evaluation was defined as F+NA, 
where F was the percentage of queries answered incor- 
rectly, and NA the percentage of queries not answered 
at all. There were two evaluations on the same corpus 
using this metric: one of NL text understanding alone, 
and the other of a complete spoken language system 
(SLS) comprised of Delphi and the Byblos recognizer. 
Our system achieved an official result of 14.7% on the 
NL test, which was the third-lowest error rate achieved. 
The SLS error rate was 17.5%. 
Our own experiments show that using the Semantic 
Linker reduced our system's error rate on the NL test by 
43%. This was largely achieved by dramatically low- 
ering the no-answer rate NA from 18.7% to 2.3%. Just 
over 80% of this increment of sentences answered were 
answered correctly, so the Linker showed considerable 
accuracy. 
9 Porting Delphi to the SPLINT Domain 
The SPLINT (Speech and Language Integration) do- 
main is concerned with Air Force units and their com- 
ponent aircraft, weaponry and other physical attributes 
of aircraft, ordnance, and facilities (such as air bases, 
runways, bunkers, etc.). The SPLINT database has 106 
fields in 23 tables. 
Some example utterances in the SPLINT domain are: 
What aircraft types are assigned to the 32nd? 
Which base has a unit carrying mavericks? 
Can a Stealth use Langley's runway 1 ? 
In order to port Delphi to the SPLINT domain, 
SPLINT-specific versions of the domain model, lexicon, 
realization rules and db-mapping rules were needed. For 
the speech-understanding part of the application, word 
pronunciations were also neccesary, as well as word- 
class membership for a statistical n-gram class gram- 
mar. Delphi includes "core" versions of some of these 
knowledge bases: a core domain model with common 
classes like NUMBER and TIME-OF-DAY and rela- 
tions like GREATER, a core lexicon with closed-class 
items such as prepositions as well as words appropri- 
ate to question-answering in general such as "show", to 
which domain-specific items have to be added. 
In porting to SPLINT, 60 classes and 65 relations 
were added to the domain model. 400 words were added 
to the lexicon. Of these, approximately half were de- 
rived from database field values. 118 realization rules 
were added. 
The grammar did not need to be modified, with the 
exception of adding one rule (for constructions such as 
"Mach 1"). 
The entire process took about a person month to get 
90% coverage on a 1400 sentence corpus, developed in- 
dependently by a non-NL person. An additional person 
week was required to develop the speech-related knowl- 
edge bases. A complete spoken language system with 
Delphi as the understanding component, plus a Motif- 
based user interface, was succesfully demonstrated at 
the 1994 ARPA Human Language Technology meeting, 
and at Rome Labs in New York. The porting process is 
described in more detail in (Bates, 1994). 
This effort demonstrates that, given an appropriate 
system design, it is possible to build a complete spoken 
language system that is robust to speech and production 
errors, and to do so rapidly and straightforwardly. 
10 Conclusion And Summary 
In conclusion, we have developed a technology that 
makes maximal use of general linguistic knowledge to 
improve portability, while at the same time maintaining 
robustness in the face of the type of input one can ex- 
pect from a real-life spoken language application. The 
system has been shown to reach high levels of perfor- 
mance in objective blind-test evaluation on the ATIS 
domain. The system has also been shown to be rapidly 
portable to a new domain, SPLINT. This did not re- 
quire any changes in the underlying system code, and 
was done with a relatively small effort. 
This work shows that computational linguistic meth- 
ods, based on general knowledge of language, can be 
used in large, robust spoken language systems, and that 
special-purpose NL understanding systems do not have 
to be built for each new task. 
11 Acknowledgments 
The work reported here was supported by the Advanced 
Research Projects Agency and was monitored by the 
Office of Naval Research under Contract No. N00014- 
92-C-0035. The views and conclusions contained in this 
document are those of the authors and should not be in- 
terpreted as necessarily representing the official policies, 
either expressed or implied, of the Defense Advanced 
Research Projects Agency or the United States Govern- 
ment. 

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