DC. rCSRTR-236 - ERIC · DOCUMENT RESUME. ED 215 313. CS 006 607. AUTHOR. Bruce, Bertram TITLE...

DOCUMENT RESUME.

ED 215 313 CS 006 607

AUTHOR . Bruce, BertramTITLE HWIM: A Computer Model of Language Comprehension and

Production.INSTITUTION - Bplt, Beranek and Newman, Inc., Cambridge, Mass.;

. u y oReading.

SPONS'AGENCY Advanced Research Projects Agency (DOD), Washington,D.C.; National Inst. of Education (ED), Washington,DC.

REPORT NO rCSRTR-236PUB DATE Mar 82CONTRACT 400-\76-0116; ONR-N000-14-75-C-0533NOTE 37p.

EDRS PRICE MF01/PCO2 Plus Postage.DESCRIPTORS *Comprehension; *Computational Linguistics; *Computer

,Programs; *Computers; Discourse Analysis; *Language. Processing; Models; Progrdm Descriptions; Program

- Development; *Programing Languages; ReadingResearch

ABSTRACTA computer natural-language system called HWIM (Hear

What I,Mean), is described in this report. Noting that the system4. accepts either typed or spoken inputs and produces both typed- and,..spoken,responses, the report proposes HWIM as an example of a

Telatilielytomplete language system ,illustrating how'the manycomponents of languasae.processing interact. The report focuses anHWIM's d4coui.se processing capabilities and on the integration -ofdilVerse types of knowledge for the' purpose ,Of comprehending andproducing natural language. 01)

0

4a

. . '.ir.

.

**********************x********.********************0*'****o***.t ,* Reproductions supplied by EDRS are.the.best that ,can. be-made *

* from'the original. document. - *

*************************,*************"******.*************************,

'

CENTER FOR THE STUDY OF READING$

Technical Report No. 236

HWIM: A COMPUTER MODEL OFLANGUAGE COMPREHENSION AND PROUCTION

Bertram Bruce

Bolt Beranek and Newman Inc.

March 1982

University of4illinoisti at Urbana-Champaign

51 Gerty'Drive,

Champaign, Illinois 61820

U yS. DEPARTMENT OF EDUCATIONNATIONAL INSTITUTE OF EDUCATION

EDUCATIONAL RESOURCES INFORMATION

CENTER IERICI

This document has been reproduced arreceived from the person or organizationortgina ling it

, Minor changes have been made to improvereproduction quality

Pomp of view or opinions stated in this document do not necessartly rep! esenpotticial MEposition or policy

or

BBN Report too. 4800Bolt Beranek and Newman Inc.

.50 Moulton Street.Cambr'idge, Mas'sachusetts 02238

.e

Bill Woods, Bonnie Webber, Scott Fertig, Andee Rubin, Ron Brachm'an,Marilyn Adams; Phil Cohen, Allan Collins, and MartyRingle contributeduseful suggeWons and criticisms to this paper. Cindy Hunt helped inthe preparation. The system development and mubh of the writing was'supported by the Advanced. Research Projects Agency of the Departmentof Defense and was monitoled by5ONR under Contract No. NO0014-75-C-0533.Additional work on the pap4;itself was supported by the NationalInstitute of Education under Contract No. HEW=NIE-C-76-0116. VJ,ews

and conclusions contained here are those of the author and should notbe interpreted as representing the official opinion or policy of.1/4DARPA,NIE, the U.S, Gerernment, or any Other person pr agency connected withthem.

r

$

2

0

*

EDITORTABOARD

Paul Scse aid' Jim.MosenthalCo-td itors

Harry -Blanchard

Nancy Bryant

Larry Colker

Avon rismore

Roberta Fetrara.

Anne Hay

Asghar Iran-Aejad

Jill LaZansky

Ann Myers4.

Kathy Starr

Cindy Steinberg,

Willi Tirre

Paul Wilsbn4

Mi:ChaellqiVens,. Editorial Assistant),*

eN

,f4

f

\ .

HWIM: A Computer Model of Language

Comprehension and Production

People design computer programs to understand natural,

Language for two.reasons. One is to make computers more useful

.

by facilitating_ communication between the computer and computer,

users. The other is that, the 'design of such programs can, inforuk:

, \theor ies.

of human language use. The program Olen becomes a model-."-

for human language comprehension or production. Decisions made

in the design process in order to enhance efficiency, or sifnply

to make the program succeed, suggest -chaiacteristics Of thei

processes. humans carry out when performing similar.taskt. Al.se,'

t......- ftthe extent to which a theory of language case actuallyit"works".

when it is implemented as a computer model, is one measure of'1 its'

correctness, Moreover, the act_ of expressing a-theory infa-,

-

correctness,°

computer program forces us to be explicit and unambiguous about1(

\what the theory is. By examining the development, the strdc'ture,'

.

and the operation of' computer programs that successfuily'use.c.

. . -r--1 natural language, one may gain insights into human use of natural

,

language, including reading and writing', speaking and listeLng.0"

v--

r This paper discusses some general issues of. language'4. .

.

comprehension and production through examination of a completel

natural language system called "HWIM" (for "Hear what I mean40.

HWIM was -developed over a, five year period a6 -the Bolt Beranek .

4 1

.

Q n.

A CoMputer Model

and Newman speech understanding system. It was designed to

understand natural language (typed or spoRen); to answer

questions, perform palculationp, and maintain a data base; and to

respond in natural language (typed and spoken). Both its inputs \

and outputs used a, relatively rich grammar with ('a 1000 wordL

.vocabulary. Utterances were assumed to be part of an on-going

dialogue so that HWIM.had to have a model of bdth the discourse

apd the user. This paper focuses on the component of the system'

embodying semantic and pragmatic knowledge. -A fuller treatment-.

of the sitem 'can be found in Bruce (ih press) and in Woods,

Bates, Brown, 'Bruce, Cook, .Klovstad, Makhoul, Nash-Webber,

Schwartz, , Wolf, and Zue (Note 9). For discussions of other

speech understanding systems, see Erman, Hayes-Roth, Lesser, and

-RedUy --(1*30);41Lea (1980) ; and Walker (1978) .

9

HWIM's Job Responsibilities

HWIM was designed to serve as an assistant for the manager,

of a travel budget. The task of. he system was to assist the

travel budgdt manager, helping to .record the trips taken or

propose and to poduce such summary information as the totalo

money 0.1ocated to various budget items. In order to carry outAll*

its duties, HWIM new to converse with the travel budget ,

manager.. ghough their conversations were simplified'relatii; to.

natural-inter-personal conversation, the:design of the system can

t

10(

1HWIM:, A Computer' Model

4

help in formulating more -genera]: models of language

understa'ndirg. Some, salient features of HWIM are the following:

o the maintenance of dynamic models of the disbourse,

the task, the user, and specific facts abouf the

worldI

o the use of such knowledge to constrain npOgsible,

,

interpretations of. utterances, thus increasing the -

likelihood 'of successful, understanding.. ,

o the use of tbe'same ,knowledge in geherating both

t.

, .

p written ,and. spoken responses; a forMalism for

r'' expressing response generation rules which provides,

_

a framework for the application, of semantic and.

.... .

t

,discourse level knowledge

.480,.$ 6

e I.o facilities for making -the, systm's own khowledge.,

.

state known to the travel budget manager ,, .

o inference mechanisms and data base structures-that

,facilitate freer expression of commands and

'questions Sy, the travel budget manager

.

. , ,.

In this= section we see(

a',trace,of HWIM1g,procesEing. ',theTh.

4. , .

sentenc'es.ihown here' would normally occur as part of an ongoing

Steps in Processing

0

6

4

HWM: A Computer Model

5

'.dialogue. While their. interpretations and resulting responses

might besignificantly different in,context,.'What :Can be seen

here the flow of control within the system, the types of

interpretations produced, the inferenC'e' Capabilities, and

response generation. The structure of HWIM is shown in Figure 1

(anc) discussed further 'in Appendix A).

'Whpn HWIM erigage4s 4n a text dialogue, the system .reduces to

just two processes, Trip and Syntax. In that mode Syntax is a, ,-

_subl3rocegs to Trip, -though it may itself invoke Trip as its own'

gubpr cess to evaluate tests on arcs in the grammar. To .see one%posgib &flow of control, (Figure 2), consider the Sentence -

/

Enter a,trip for Jerry Wolf to New YOrk.

Trip reads the input and does a morphoi.ogical.' analysis of each,

word. It then calls Syntax to arse and interpret the Sentence.I e

During processing, Syntax may enco nter a test, e.g.', "Does , the

'name 'Jerry' Wolf' denote a know person?", which is to be

performed by Trip. ,Control passes to Trip ich answers 'yes,"

and then, back to Syntax" again. When the parse is Oimplete,

control returns to Trip., Ambiguity, in the input can 'cause a

.question to be formu.lated for the travel budget manajCr. The

managerJs'response would cause a return,to Syntax, and so on to,

the end of the s-e,ssion.'4

.

t

e9

4

)

a

if

t

I

&

a

wit

- .

HWIM: A Computer Model

r

4."

6'

SEMANTIC NETWORK

BASE

DICTIONARY

INTERPRETER TRIP

4119;41110WDICTIONARY

EXPANDER

,

EXPANDED DICTIONARY"

IL' SPEECH

imrtODERSTANDIgGs.

GRAMMA R

..

VERIFIER

PHONOLOGICAL

RULES

1

SPEECH OUTPUT

PARAMEA ICREPRESENTATION

OF SPEECH-SIGNAL

TALKER

e.

.

Figure 1. The seructure "of -HWIM.

1

r-,.

4..8

119

W

SPEECH INPUT- ..

4

l

i

9

Is

7) -04

sI

1

1. "Enter a trip for .

Jerry Wolf to New York."

Z. PARSE

((ENTER A TRIP...))

FIVTIM: A Compute r Model

7

dr.

6. "Ok."

igurt e 2.

3. GOODNAME?

(WERRY'WOLFLY

5. (FOR: --

(BUILD: TRIP --) )

One possible flow of control for a dialogueinte rchange.

-4

4


8

To see the steps,HWIM tak n reading, comprehending, and

then respOnding to some- text, imagine that' the travel budget,

nager types:

When did Bill go to Mexico?

The program makes a fist pass on the'Enput, converting the words'

as typed into words as they appear in its dictionary and removing'* e 1

' punctuation. For example, "$23.16" would become "twenty-three

dollar-s and sixteen cent-s". In this case the modified word

list is simply

.(WHEN DID BILL GO TO MEXICO)

For spoken inputs HWIM has to consider, many possible-word

lists beCause the inpiA is ambiguous. This could be viewed, as

. analogous to problems faced by a person in reading (see

Rumelhart, 1977; WOods, 1980b) . For typed' inputs; HWIM is a

perfect decoder; thus, only one word list needs to be considered.

In order to simplify the discussion, the example here will focus

on a single word list.

The parser in HWIM uses a pragmatic grammar which contains

significant static knowledge of' the travel,budget management

domain. Collapsing the task specifiC knowledge into the gramm

was viewed as an 'expedient to gain computational (processing).

.

efficiepcy. It helped to constrain, parses early and .enabled

s'f

Ot

HWIM: A Computer 'Model

9

A

risimultaneous parsing and semantic interpretation. This is in

contrast with the two phase method used in the LUNAR parser%

(Woods, Kaplan & 'NashWebber, Note -10) which produced purely,

syntactic parse trees that were subsequently given to. a semantic

interpreter' to tansforit into executabld interpretations. More

recently, techniques such a& the use of .cascaded ATN's (Woods,4-

1980a) as in the RUS parser (Bobrqm,4_Note 2)_ have been developed

to gain much of the efficiency of pragmatic ors semantic grammar

while maintaining- a clean separation between,general syntactic0

loowledge and task specific knowledge.

For the example sentence, the parser derives ,the 'syntactic,

structure:

S. Q -"

QADV WHEN

SUBJ NPR BILL,

AUX. TNS PAST

VOICE ACTIVE

VP V GO

PP PREP. TO

NP N ?R MEXICO'

ADV THEN

Af

Taking advantage of the. semantic knowledge in the grammar, we get,

t V..

;

in addition:

HWTM: A Computer, Model,

[FOR: THE A(/00.9 / (FIND: LOCATION (COUNTRY 'MEXICO))

10

: T ; ,(FOR: 'THE A0008 /*JFIND: PERSON JVIRSTNAME 'BILL))

6T ; (FOR: ALL A09104/ \-

(FIND: TRIP (TRAVELER A0908)

*(DESTINATION .A0009)

(TIME (BEFORE NOW)))

: T ; (OUTPUT: (GET: A0010 'TIME]

The interpretation is, a quahtifthd expression which can (almost)

-be directly executed. Translated it says, roughly: :1

.

Find the location which has the Country , name ' ,,

-,,

/ ..

"Mexico" and call it A0009. Find the person whose first',

mane is "Bill" and call him A0008. Find in turn, all

trips. whose traveler is A0008, whose' .destinatio is

A00b9, and which occurred prior to now. At each is

enumerated,' label it A0010 'and output its specific,4

.

time.4

The T's which appear in the' fnterpretation are there in Place of.

potential restrictions which might have been applied to the°

,1-location, person, or trip classes being sealched.

1Before the interpretation can be executed it undergoes an

optiii.zation (See Reiter, 1976) and modification ,to match the

't ,

r

°

.HWIM: A Computer Model

11

data base represientations. In this example ,there is no entityicalled "LOCATION' n the Semantic netx.ork data base which serves

, the purpose implied in the ..interpretation. Theresvare' cities,states, coasts, countries, etc. and there"LOCATION", but, no concept with instances at we might expect. Theinterpretation thus references a fictitious mode. The optipizerrecognizes this and modifies the inte rpretation so that a searchis --x-fone among all the possible "locations ". . In HWIM, this

is a link called

modification is speciftre to the fictitious node, that has benreferenced. More recent knowledge representation systems, such

as KL-ONE would formalize,the ability to form. abstractions suchas . "location" from. the more specialized concepts (Brachman,

Bobrow, Cohen, Klovstad, Webber, & Woods, Vote 3):

The optimized itterpreitation is placed on a queue of

to

-be- executed commands. The queue represents a firstt reSteptowards a "demand model of discourse" (see next section) . Inprinciple .it can contain previous queries or commeands,from the

,

speaker as well as, system initiated 'co ands. However, most

utterances9 translate into single commands Which are directly-.

executed.

-The (FOR:*--) expression above, is ,a LISP form which can . be

evaluated directly. The FOR: function manages quantification inthe expected way. Here it looks for a unique Ideation and a

/


12

uaique person ,which match the respectiv descriptions. There is a

single place whose country name is "Mexico," i.e., the country,

Mexico. however; there are 8 items in the data base representing

people whose first name is "Bill." Given the apparent

inconsistency between the data base and the command, FOR: is

forced to take the initiative of the dialogue and ask the manager

for help. The response generation program produces the question:

4

I55-7517 meanBilly Diskin, Bill Huggi*ns,4Bill

Levison, Bill Patrick, Bill Plummer, Bill Russell, Bill

Merriam, or Bill Woods?

When the system asks a question (a4,in Alhfs example) it

expects an answer. This means that otherwise "incomplete"

utterances may be accepted. Here the parser will allow a name aS

a complete utterance. In this context, the person types a secondto,

sentence:

Bill Woods.

This sentence undergoes a pre-pass to produce the word list

(BILL WOODS)

which the parser inEeprets as

(! THE A0011 PERSON ((FIRSTNA BILL)(LASTNAME WOODS)))

14

.:

The optimized interpretation is17 .

e .0r


(IOTA 1 A00 11./ (F/ND: PERSON4

(FIRSTNAME 'BILL)(LASTNAME 'WOODS),1,: T )

This interpretation uses the'IOTA operator, a function that

13'

. 4

returns the one item in the class defined by the (FIND: - -)

expresion which meets th4 restriction, T (i.e., no re,striction).

In this case there is .exactly one item matching the description,

namely BILL WOODS.

. ..

At this point, we are n the middle of what Schegloff (1972), '

.,has called an "insertron sequence." In'the piOcess of answering

'the user's question, 'the ,system began executing a4.

FOR:, txpresiion. EvalUating 'the FOR,k expression required

information that was obtainable only by asking the ,user a

question. HWIM's.question to the user, plus' the 'user's answer

constitute an il;csetpn sequence with respect to the primary'

question - answer sequence. The execution of the °FOR: expression

is' suspend&1 for the duration of the 'insertion sequence, but cah

be resumed. when' the /1.1.-.eded information is processed. Having nowresume.'I

4 %a unique :VISTINATION and a unique TRAVELER, FOR: can find all

.

- trips whose 1. is (BEFORE NOW) and for each; output its TIME. 0

.11

Finding 'the trips, checking TRAVELER, DESTINATION, and TIME

can 'require corisiderablesearch' in the data base. For example,

I

4

,r

HWIM: A Computer,Model

14

the DESTINATION of a TRIP is computed from the DESTI-NATIONs pf

its component LEG/OF/TRJPs. The DESTINATION of a LEG a /TRIP may

have to be computed from the PURPOSE of the LEG /OF/TRIP,

attending specific ,,.conference.. Information about trip

budgets; conferences, etc. is never assumed to be compleee s.ince

it is not So in the real world. Furthermore, the range

askable question& precludes cOmPluting in advance all the implicit

information. (The procedures .that do these compdtations are

giscuss&Lin Woods et al., Note '9.) In this example there is

'only one trip thatjOtches the description and.its,tdme is

printed out by the response generation 'programs:

Bill Woods's trj.pl from Boston, Massachusetts to

Juarez, Mexico was from October 15th to 17th, 1975.

-- Discourse Model%

In any conversation there are' ,many 'forces operating to

determine what will be said next, including the ,memories, the

intentions, and the perceptions of the participants in the., )

..

conversation` (Bruce, 1980, Note 4). A discourse model/ is an

idealized representation of such forces as they are used by a,

speaker in. constructing ,an utterance and by a listener in.,-

understanding one. . (See also ReichrmaA, 1978; Stansfield, 1974;

Deutsch, 1974; Sidner, Note 8).

;

16

1

i.


'15

The discourse ,context is an important factor in even the

restricted domain of persona- computer communication, but in many

_interactive natural language Understanding systeffis there, is no

explicit "discourse molder". °-Mostartificialproblem domains are

deliberately constructed so that only a few interaction modes are

°Sensible.. For example, th.e person may only ask questions;,the

°system may only answer the questions. This greatly simplifies

the determination 40e the speaker's intent, and hence, the full.

understanding of the utterance. There is also no need too

maintain a data base of'patticipants in a conversation (together

with their presumed pUrpose,! attitudes,- model's- of the 'world',

etc.) when the only other participant .in the-convesation is "the '-\

USER.". Even in those cases where the problem d f'nition permits

'a general discourse and tie systeM to cd with that

generality, the means o coping are rarely deli eated (Bruce,

19756..

Incremental simulations (see Woods & MakhOul 1973) of

dialogues between a travel budget' manager and HW M (a system,

builder -played the part of an ideal HWIM) suggested a discourse

model in which, at any point, the manager could b se :n as being

in one of several States, e.g. , trying to deter ine the

consequences of a proposed trip, examining the :St to of the

budget, br entering new trip information. We considered, several-'

forMulations of a discourse model in which these states would be

4.


16

made explicitand used to advantage.

One 'such formulation involved the concepts of m'odes of

interation and, intents% (Bruce, 1975c) . An .intent is the assOmea

purpose behind an ,utterance. Patterns of intents such as,

user,- enter-new-information-

system-apoint-out-contradiction

user-ask-questiron

system-answer-question

use ing-tangesystem- confirm - change,

.

constitute the modes of interaction.

k

. An augmented transition network (ATN) grammar was used to

represent same f the common 'modes of interact -ion found in travel'

budget management dialogues. Then Na modified ATN' parser 'was

written that steps: through the graMmer on the basis of the input,. ,r

sentence structure and the then-current State of the data base.

At any givents state the parser can predict the most likely next

intent and hence such things. as the. class of likely verbs for the

next utterance. This model was not used in the final version of,

the system, primarily because it was too rigid to function alone .ada discourse model.

Another formulation of the .user,/discourse model involves the

18

O

-HWIM: A 'Computer Model.

17

notion of demands made by participants in the dialogue. (Both of

these formulations are discussed more fully .;r1 Bruce, r975b).

Demands include _such things as unanswered questions and

contradictions which have been pointed out. This latter

formulation allows us to model how one computation of a response

can be pushed down, while a whole dialogue takes place to obtain

missing information, and how a computation can spawn subsequent-

expectations or digressions. Some elements of this deld model,

together with other aspects of the discourse model, are explained

below:`.6

(1) Demands: These are demands for service of some 6,,or''t

'made upon the systemby the user or by the system .itself. An

active unanswered question is a typical demand with high

priority. The fact that some questions cannot be -answerdd

without more ingbrmation leads to an embedding of modes of

-interaction. 'Demands of loWer priority include such things as a

notice by the system that the manager is over his budget. Such a

notice might not be communicated until: after direct questions had -

been answered.I 4.

'ea(2)' Counter - demands: These are questions the system has

explicitly or implicitly asked tq- user (e.g., "This trip !dill

put you over budget" implicitly asks the user to review the

:"

budget, canceling budget ?items or adjusting cost -estimatet) .

6'

A


18

While it should- not hold on to these as long as it does to

demands, nor expect too strongly that they will jbe met, the.0*

system can' reasonably expect that most counter- demands will be

resOlveCl'in Some may. Phis is an additional influence on the

discourse structure.

(3) Current discourse state:, The discourse area of the data

baSe also contains an assortment of items that define the current

discourse context, .including:

o LOCATION, a pointer to the current location of the

speaker, e.g., the city

.

o TIME, a pointer to the current time and date

o SPEAKER, a pointer to the current speaker

o the last mentioned person, place, time, t-eip,

budget, Conference, etc.

There is also a representation of the current TOPIC (see Figure. s \

3). This is the active focus of attention in thia dialogue. It'1

could be the actual budget, a hypothetical~ budget, a particular

trip, orja conference. The current topic is used as an anchor

point for resolving definite references and decidion hoW much

detail to give in responses. It cap also generate' certain modes

of-Jilteraation. For example, if the manager says "Enter a trip,". ,

4,0

11,

DISCOURSESTATE

A

INSTANCE/OF

HWIM: A Computer' Model

19

. )

SUBTOOIC SUBJECT-

SUBJECT

Figure 3. A discourse state.

r

4

21 s.

INSTANCE/OF

INSTANCE/OF

a,

NC

's

.1 I . . 4e 0, -. I.. .. \

., .

. , HWIM: A Cbmputer- Model0 ,,

' !.. 20.

. the 'system notes that_ the current 'topic' haS changed to an

I

..

incompletely described trip. This results in demands that cause.. /

,standard fill- qn questions to be asked.' -If lie. manager wants to.. .. .

,.

,

complete the trip descriytion later/ then the completiliof the., ,2t 4 o

P - 1 z','trip description becomes a low priority demand:. ',,,

,. . .

The system has a praitive one-queue implementation ot'the ..-..

' -'. ,.

, . 6-

"demand model. This queue contains executable procedures which4%,

represent the speake'r's previous queries and commands as. weld. as

commands initiated by the syStim to examine the consequences of

sits actions, give information to the user, or ch'eck for_data bass. , ,

consistency. These procedureg are pelatea by functional

dependencies and relative prioriti*es. The major .types Of demands

are' the following:

V

o, DO: means execute the specified c mmand.

0" TEST: means evaluate the form and answer "no", if NIT/

or ".yes" dtherwdse.

RESPOND: means give the user some information (which

may or May not be part of an 'answer to a direct

quvry).

4

o' PREVENT.: means monitor; for a subsequent, possible

action and block its .normal, execution (asi.r1:"Do not

allow more than three trips to Europe."}.

. "..

2

r-

a.

r-

HWIM: A.Computer Model

21

Understanding

The process of understanding written natural language. ,

i'requires the integration of diverse knowledge sources. An op mad.

prdCdss must Apply various types &-t-knowledge.in a balance hat

avoids over or undenLconstraining the set-of potential account

of the input.

Ultimately the understanding process should produce a. - - 2.

_ Meaning representation jor the natural language input, whe,t,her.

, -the. input be written or spoken.. This representation May be

. 1 .produced directly or via some number of contingent knowledge

....., .'

,

structure's (Bobrow El.-Brown, 1975). The exact form of these,

structures .vpries from one system to,another, but tyPically

includea such thingsas'parse trees.

In HWIM, integration of knowledge. sources for linguistic

L_pcocessing- is. accomplished by means of °(1) an ATN grammar (see.4.

,

,.muchFigure that incorporates much of the syntactic, semantic,

. and

pragmaticknowledge i' .the system, '(2) a semantic network (see

stash- Webber, 1975Y that represents remaining linguistic and world

knowledge, and (3) tests on arcs in the grammar used to link the

two representations. These structurps are used to produce the4

account of the input that serves: as a representation of its4

meaning.

..,

r

A

/

I

OA

;

JUMP ,

.

HWIM.: A Computer Model

/

A

. 22

(C..

.

POP

-AINTERP=

(I THE-LOCATION-)

NPR

\.6s

0 CALL TRIP FORK TO VERIFY THAT THIS MATCHESp .."

NI._

"ill' 4...

/

A-

.s,

"\.

..,-

cJigure 4. A small'poition of the pragmatic grammar. .

.........-----.... 0

274

..-

A

-7

HWIM ,A Computer Model

23 c.

The decision to include semantic knowledge-in a. grammar has

'both.its merits and' its defrcts. On the one hand there is an

obvious- advan age in terms of efficiency when semantic knowledge'

+Cali be applied early ondin the most direct way to constrain .theh' r-

possible interpretations_ of an utterance. Moreover, one avoids

the seemingly u'nnecessary step of building a syntactic., model,4

.

such as a parse tree, for an utterance. Ala of this points to

the desirability of a 'undfied linguistic processor as exists .in

HWIM (see also Erman, Hayes-Roth, Lesser, & Reddy, 1980;,Burton&

Brown, Note 5) . On the 'other hand, the fact that we could

incorporate semantic and pragmatic:knowledge in the grammar' and

Use that knowledge as if'it were fundamentally no different fr4om,

syntactic knowledge was a consequence of having a limited -domain

of- discourse. No computer natufal' language system has' yet

approached the variety and complexity that natural human4

'cbmniunication exhibits. As semantic and pragmatic knowledge

becomes more 'complex,' more.' fluid; and more' intricately

interconnected, it not clear to what extentthe pragmatic

gramm'ar approach will' work.

Despite the inclusion of non-syntactic knowledge, the

pragmatic grammar is not a comprete knowledge source by itselfN

for linguistic processing. It is, however, closely linked 'to the

semantic network via tests on the arcs These tests allow the

sqrammar to capture more volatile coniptraints on the input, such

HOIM: .A Computer Model

, 24

as those provided by the discourse model or the factual data

base. .For example:

1

o Is. "Jerry Wogfas" a valid .name?-0" #0

0 .Is 4an Francisco,.California" place?

o What words can 'go with "speech", as a project

descriptoi (e.g., "understarVinIg")? ir /o isDoes "What s the registration fee?" make sense in

this context?

o. Does "How much is in the budget?" make -sense in this

Context?

o Dcre-"When is that meeting?" Bake sense in thiS

context?(7?'

Does "November 15th" make sense in this context?

3

With the grammar encoding semantic and pragmatic, as well as

syntaC tic information, it is.possible for_the parser to build a

procedural" "meaning" representation as well as a purelysyntactic

one. The meaning repfesentations are in a command languAge whose

-expreSsions are atomic, consisting pf, a-functional operator

- applied to arguments, dr compound, making;u.se of a duantification

operator (FOR:) applied to another' expression. TIN operators in.,;

2t

O


25 .

atomic expressions specify operations to be performed on, the data'

Uase or interactions with the user.

The parser builds interpretations by accumulating in

registers the semantic head, quantifier, and links of the nodes

being, described in the sentence (see Woods et al., Note 9, for a

more.complete description) . For exempt, the sentence

go to the ASA meeting.

yields an interpretation in the command language (Section

COMMAND) of the form

(FOR: THE A0018 / (FIND: MEETING (SPONSOR 'ASA)) : T ;

(FOR: 1 A0019 / (BUILD: TRIP

(TO/ATTEND A0018)

(TRAVELER SPEAKER)

(TIME (AFTER NOW)))

: T ; T))'0

Thiq interpretation

constituent describin

is built up in the following way.

er son i,sfoundat.---the-.-:st-a-r--t-7-o-f--the

sentence. The. parser transforms, the pronoun. "I" into. the

link-node pair .(TRAVELE SPEAKER) and returns this as the

interpretation of that constituent. The word "will" adds (TIME

(AFTER NOW)) to the list of link-node pairs being accumulated.

(The grammir does not accept constructions like "will have gone,"


26

so "will" can always be interpreted'as marking a future event.)

The word "go," in the context of our travel domain, .indicates

that a trip is being discussed. Next, the constituent' "the ASA

meeting" produces

(TO/ATTEND (1 THE Y MEETING ((SPONSOR ASA)))).

(The ! indicates that as FOR: expression-Lwill have to be built as

part of the interpretation.) The top level of the grammar has

thus accumulated the link-node pairs

(TIME (AFTER NOW))

(TRAVELER SPEAKER)

(TO/ATTEND (! THE Y MEETING ((SPONSOR 'ASA))))\.

with the semantic head TRIP. The appropriate action (in this case

a BUILD:) is created, to produce

(BUILD: TRIPgo (TO/ATTEND (! THE Y MEETING ((SPONSOR,'ASA))))

(TRAVELER SPEAKER)(TIME (AFTER NOW)))

O

Finally, the necessary quantificational expressions are

expanded around the BUILD: expressions

Response Generation

Once a satisfactory theory for an utterance has been

constructed, it must be acted upon by the system. Regardless of

28.

.fr

O

.0_ HWIM:' A Computer Model

27

the type of action taken, some appropriate response-should also

be made. From the speaker's point of'view-the response should be

explicit, 'concise, and easy to. understand. he response may-1

affect1/4the speaker's way of describing entities in dOlpain asvs

well as illuminate the capabilities and operation of the system..,

° The effects of generated responsesre many. , In a domain

suchu as travel budget management there are many objects without

stanard names, e.g. , "the budget item for two trips to a West

Coast conference". Names chosen (constructed) by HWIM provide a

handle for the manager to use and thus strongly influence the

ways the objects are referred to- subsequently. Responses can

also indicate the capabilities of the program. tareful

construction of responses to exhibit' exaCtiy the knowledge

structures handled' by HWIM gives the managei the legitimateIN

confidence to pursue just the.paths ,which rely on those

structures: A response can also show the system's focus of

attention. the manager can thus get -a better idea, not only of

the facts 'she or he. seeks, but alo of how WelltheSY-SteliSA

understanding and wheremore-clarificatiod May be useful..4,

Types of Knowledge Used in HWIM

order to carry out.its role eiffeCtively, HWIM -must have. .

large amount o knowledge in a readily accessible form. This

knowledge is needed-for understanding both spoken utterances -and

a

HWIM: A Computer

28

written sentences, carrying out commands, answering questions,

and generating responses. HWIM's success in performing these

tasks is evidence that the types of knowledge embodied in the

system' are sufficient foriatural communication, at least at the.

level shown by sample dialogues. The-history of the development

of HWIM (N41,Webber & Bruce, 1976) suggest's that each. category

of khowledge is also necessary for natural communication to take-c.

place. The kinds of knowledge used by-HWIM can be categorized as*

follows:a

o Acoustic-Phonetic forms--Knowledg

their relation to aqodstic paramete

Phonological rules--KnoWledge of

and pronunciation variations describable by rules. .

phonemes and

honeme clusters !

o Lexical forms--Knowledge 'of words, their

inflections, parts of speech and phonemic spellings.

For example, "entered" is the past form of "to

enter".

A 0 Sententi41, forms--Knowledge. of1 p,

grammatica

structures at the phrase, clause, and sentencer .

level. For example, a sentence may start with a

dommand verb, such as "schedule:."

o _II-iscourse form--Knokg.edge of idealized discourse,

II

41


./"Q4g.,- what type of utterance is likely to

produced i a given s e of the discourse:

Seman is -- Knowledge of how words are related and.

used and structurally conveyed meaning. This is

uted fn parsing and in constructing responses: For

example, the benefactive case for'schedule:should be

29

'filled by a person who will be taking the trip being

scheduled.

"1:3 World--Knowledge of specific projects,' trips,

budgets and conferences. Whereas HWIM's semantic

knowledge is essentially fixed, its world knowledge

changes every time a trip is taken or money isIy

shifted from one budget item to another.

o On-Going discourse -- Knowledge of the current

discourse state, e.g., 'the current ptopic and the

objects available for anaphoric reference. This

knowledge is used to relm! constraints °in the

pragmatic grammar. For example, if a conference is.

under, discussion, then "What is the registration

fee?" is a meaningfdl utterance. If dot, then the

speaker would have to say something like, "What is

the registration fee for the ACL-conference?"

4)

1

t.

HWIM: FIComputer Model

30

,o Parameters of the communiaative'situationHWIM's

operation is a function of the modality in which it

operates;' speech of text input ,and speech or text

s output. In general, communicative situations can

vary along.a number f dimensions (see'. Rubin, 1980)

and a successful communicator must-use knowledge of

the situation to interpret and respond

appropriately.

'o User-Knowledge about each p iblv travel budget

manager, what groups .they belong to, and what they

may know about the'data base. For example, only

certain users may be allowed to modify budget

totals--

o Self-,Knowledge about the system's own knowledge

((often .called "meta-knoWledgeq. One example is

that data on trips and budgets is marked to indicate

-whether it came frol4 the travel budget manager or

was computer by HWIM. Anotha is that HWIM knows

what elements constitutfa.a complete description of

any object, such as a tr ipp Thus it knows when itse.

OWQ knowledge is incomplete.

o Task--Knowledge of the riask' dom'ain, e.g., that a

manager is Concerned with maintaining an accurate

tL

a HWIM: A Computer Model

31

record of all trips, taken or planned, and with

staying within the budget.

o Strategic--KTwledge'concerning the representation

and. use of other knowledge. This knowledge that

would be needed in even a "blank" systeml'i.e., one

that had no word- or domain-specific knowledge.

Conclusion4

One reason for building a computer model of language

understanding is that in the course of designing and debugging a

computer program, one must resolve theoretical question4rabout

details of the process that can be glossed over in less

procedure; oriented models. For example, desijners of computer ,

models of speech act generation (Cohen & Perrault, 1979) have

increased the prdcision of speech act definitions. A second

reason for computer models is that the task 6f the system can be

defined to require cdmplette use of language (wfthin a restricted

. domain, of course). . Thus,-one can See what components, each

repreSenting .aspects of language theory, are needed-and how they

might interact. Winograd's (1972) blocks world program, a system

which accepted typed questions,. commandsA , and assertions4..,

performed actions,' and generated natural' language responses,

could'' be put in the latter category. There is value in both of

these approaches; in fact, they tend to -complement each other,

0

350

.

HWIM: AComputer Model

32*

with the second showing What can and cannot be done and the first

addressing specific design questions.4

g in both categories, but its principal value probably,

lies more in 'the second till the first. To some extent, it

representsa synthesis of a number of lines, of research in areas

such as parsing, inference,- data base design, and generation. It

shows what can be dbne in a fairly, ompletz system' that

understands'natural language (typed or spoken), answers questions

and performs various actions, and responds 'in natural language .

(typed, and spoken).

blems that arose in the design of HWIM 'were precursors of

those t are central issues in AI research today. For example,

the speech act issues for HWIM are similar to thOse studied 'by

Cohen (Note 6), Cohen .and Perrault (1979), and A114n (Nlote 1).

Questions bf knowledge representation closely related to those

fAced in HWIM have been.puvsued by Bobrow, and Winograd,. (1977),

,,,Brachman (1979), and Fahlman (1979). (Also see Brachman &' Smith,,

J.980). Language gerneration in a discourse context similai to

HWIM'sas been studied by Mdbonald (Note 7), for instance. &

Finally, the issue of interactions among syntax, semantics, ,ond

pragmatics As crucial in the work of many; inqluding Schank and

Abelson (1977) ,. Woods (1980a) , and Bobrowi (Note 2). The

characteristics of HWIM reflect the goal of natural communication

34

HWIM: A Computer Mod4el

33.

,t

between a person and a computer assistant. Even in its limited. rs

domain, it illustrates the extent to which natural Communication' v. ,,.

s,r,

depends upon diverse kinds of knOwledge in bath communicants.,

. 1The structure of HWIM can. provide a 'useful framework for

.

''.

obtaining abetter undefstanding of:rtatuxal communication.,

35

33.WM' A computer Model

34

Reference

. 1.1. Allen, J. A plan- ased Approach, to speech act iecognition.. . az. 7

3(Tech. Rep. No. 13J/79) .; Tororlto: tYn pre r.s i tY of Toronto,Department of Computer ScienCe, January 19,79.

-

2. Bobrow, R. J.

no

The RtJS system . (BBN Report- No. 3878).Cambridge, Mass.: Bolt Beranek and Newman Inc.,

3. Brachm R. j:; Bobrow, R., Cohen, P., Klovstad, J. , Webber,B. L., & Woods, -W. A. . Research in natuAl languageunderstanding Annual re.Rort (1 Sept. 78-31 August 19.79)'.

, .04. Bruce, B. et. Belief systems and language understanding BBN-

Report No. 297J). Cambridge, Mass. Bolt Beranek And

Ne man, Inc. , 1975.- (a)

5t: Burton, R., & Brown,. J. S. Semantic grammar:, A Techique/ . .

p* for ponstr uc tin,g natural language:interfacesto, instructionalsystems (1EQ3N: Report No. 3587.) . Cambridge, Mass. Belt

.

.Beranek and Newman Inc., May 1977...-

0 . J6. Cohen, P. R. On knowing what to 'say: Planning speech acts

(Tech. Rep; NQ 118) . Toronto: Department of CoMpti,ter

Science, University 4of Torono, 1978.

a6

..HWIM: A Computer Model/

35

7. McDonald, D. 'Natural language production as a process of

decision making under constraint (MU AI Lab Tech. Rep.).

Cambridge, Mass.: Massachusetts Institute of Technology,

148o,Ms'

8. Sidner, C. L. Towards a computational theory of definite

anaphora comprehension-in English discourse (MIT AI LablTech..c

. awiro.

Rep. No. 537). Cambridge, Mass.: Massachusetts Institute of

Technology, 1979.

114,

9. Woods, W. A., Bates, M. Brown, G., Bruce, B., Cook, C.,

Klustad, J., Makhoul,rJ., Nash-Webber, B., Schwartz, R.

Wolf, J., Zue, V. Speech understanding systems: Final

technical progress report (30 October 1974--29 October 1976)..

Cambridge, Bdlt Beranek and Newman Inc., 1976.

/

Woods, W.A., Kaplan, R. M.; CNadh-Webber,. B. L. The, lunar

science natural language inforqation system: Final report

(BBN Report No. 2378) Cdmbridge, Mass.: Bolt Beranek and

- Newman, Inc., 1972.

a'

37

a

,HWIM: A Computer Model

36

Ref 4

Bobrow, D. G., & Winograd, T. An overview of ERL, a knowledge

representation language. Cognitive Science, 1977, 3, 3-46.I

row, R. J., & Brown, J. S. Systematic understanding:

Synthesis, analysis, and, contingent knowledgein specialized

undeKstanding systems. rri D. G. Bobrow & A. Collins (Eds.)

Representation and understanding: Studies in cognitive

science. New York: Academit Press,'1975.

Brachman, R. J. On the epistemological status of semantic

networks. In N. V. Findler (Ed.), Associative

networks: The representation and use of knowledge in

computers. New York: Academic Press, 1979.

'Brachmaril-R. J., & Smith; B. C. SIGART Newsletter, Special{ Issue

on Knowledge Representation, No. 70, February 1980.

BrUce, B.C. Pragmatics in speech un Proceedimgs

Fourth International Joint Conference on Artificial

Intelligence, TbiliSi, USSR, 1975.(b)

Bruce,- B. C. Discourse models and language comprehension.

American Journal for IClomputaional Linguistics 1975, 35,

19- 35. {c)

HWIZT: A 'Compute r Model

37

Bruce, B.C. Analysis of interacting planS as a guide to the

uqderstanding of story struCtuge. Poetics, 1980, 9,

29'5 -311.

Bruce, B. C. Natural communication between person and computer.

In W. Lehnert and M. Ringlel(Eds:), Strategies for natural

language processing. -Hillsdale, N.J.: Erlbaum, in press.

Cohen, P. R., &Perrault, C. R. Eletents of a plan-based theoryc

of speech acts. ,Cognitive Science, 1979, 3, 177-212.

Deutsch, B. G.The structure of task oriented dialogues. In1.- OF

. ,L.D. Erman (Ed.), Proceedings of\ the IEEE Symposium on

Speech Recognition, Pittsburgh, PA: Carnegie Mellon,

University, 1974.

A

Erman, L. D., Hayes-Roth, F.,lLesser, V. R., & Reddy, D. R. The

Hearsay-II speech-understanding system: Integrating

knowledge to resolve uncertainty. Computing Surveys, 1980,

12 213-253. \\,!,

Fahlman, S. E. NETL: A system for representing and usingcereal-world knowledge. Cambridge,, Mass.: MIT Press, 1979.

.

4ba, W. A. (Ed.) Trends in speech recognition. Englewood

Cliffs, N.J.: Prentice -Hall, ,1980.

0

3

HWIM: A Computer MOdelr 38

Nash-Webber, B. L. The role of semantics in automatic speech

understa4nding. In D. g./ abbrow & A. Cpiiins (Eds.) ,

Representation and understanding: Studies in cognitive

science. New York: Academic Press, 1975.

Nash-Webber, B., & Brice, B. C. Evolving uses of nowledge'in a

speech understanding system. Proceedings Of the COLING-76

Conference, Ottawa, Canada, June 1976.

Reichman, R. Conversational coherency. Cognitive Science, 1978,

2, 283-327.

Reiter, R. Query optimization for question - answering systems.

Proceedings Hof the COLING-76 Cclriference, Ottawa, Canada,-

' June 1976,

Rubin, A. D. A theoretical taxonomy of the differences between

oral and written language. In R. J. 'Spiro, B. C. Bruce, and

W. F. Brewer (Eds.), Theoretical issues in ileading

comprehension. Hillsdale, N.J.: -Erlbaum, 1980.

Rumelhart, D. E. Towards an interactive model of reading. In

S. Dormic (Ed.1, Attenti.cn and performance VI. Hillsdale,

N.J.: Erlhaum, 1977.

Schank, R. C., ,4 Abelson,. R. P. Scripts, plans, goals and

understanding; An inquiry into human knowledg. structures.

Hillsdale, N.J. Erlbaum,`1977...

4y

. ,

t

6 HWIM' A Computer Model

41.

39

Schegloff, E. A. Notes .on a conversational practice:

Formulating place. In D. Sudnow Ed.), Studies in socialI a

interaction. New York: Free Press, 1972. .

-Stansfield, 0", L. Programming a dialogue .teaching situation.

Unpublished doctoral disstrtation, Univefsity of Edinburgh,

1974.

Walker, D. E. (Ed.) Understanding spoken language. New

ElSevier°North-Holland, 1978.

Winograd, T. Understanding natural language. New York: Academic

Press, 1972.7,

Wobds, W. A.." Cascaded ATN Grammar'S. American Journal of

Compueational Linguistics, 1980, 6, 1-12. (h)

Woods, W. A. Multiple theory formation in high-leVel perception.

In R. J. Spiro, B. C. Bruce, & W. F. Brewer (Eds.),

Theoretical issues in re ding compreheh'sion. Hillsdale,

N)J.: 'Erlbaum, 19.80. (b)

Woods, W. A., & Makhoul, J, Mechanical inference problems in

'continuous4,speech* understanding. Proceedings of the Third

.e,//

,

Jnternatfonad Joint Conference on 'Artificial Intelligence,.

Stanford,, Calif., 1973.

41 .55'

o °


APPENDIX A

The Structure.of HWIM

40

The structure of HWIM is shown in Figure 1. Components of

the system are shown in ovals with thick arrows representing flow

of control between components. Major data structures are shown

in clouds with thin arrows indicating data flow. The system is

implemented on 'TENEX, a virtual memory time-sharing system for

the DEC PDP-10. Each component exists in a separate TENEX

proceqs (called a "fork"). Among the reasons for this

multiple-process job structure .(see Woods, et al., 1976) are that

most of the components ark so large, in terms of program and data

40structure requirements, that they cannot exist in the same TENEX

address space with another component. .

The Trip component is the one primarily discussed in 'this

paper. It controls the system and is the major component for

acting on andresponding to an utterance: If it`' is given a typed

input, it calls Syntax to parse the sentence. Otherwise it calls

Spillich Understanding Control.`I

, Most of the other components perform dingle functions:

Dictionary Expander expands a dictionary of baseform

pronunciations using a set.of phonological rules (applied once at

system loadup time) . Real Time Signal Acquisition' (RTIME)

42'


41

,

digitizes and stores the speech signal. Signal Processing (PSA)

converts the speech signal into a parametric representation.

Acoustic-Phonetic N:R§ssTizer (APR) operates on the parametric

represenq,tion of the speech signal to produce a set of phonetic

_hypotheses represented in the segment lattice. Lexical Retrieval

searches the expanded diCtionary and matches pronunclotions

against portions of the segment lattice. Verifier generates an

idealized spectral representation of a word (or words), using a

speech synthesis-by-rule program and matchesit against a region.

.

of the parametric representation of the speech.signal. Syntax4

.judges the grammaticality'of a given word sequence; predicts

possible extensions at each end of the'sequence; builds a formal

representation of an utterance. Interpreter (present in an eatly

version only) takes a syntactic representation of an utterance

and builds a' procedural representation of the meaning. Talker

generates speech from phoneme-prosodic cue strings.

IP

Q._

DC. rCSRTR-236 - ERIC · DOCUMENT RESUME. ED 215 313. CS 006 607. AUTHOR. Bruce, Bertram TITLE...

Documents

Transcript of DC. rCSRTR-236 - ERIC · DOCUMENT RESUME. ED 215 313. CS 006 607. AUTHOR. Bruce, Bertram TITLE...