Table Of ContentSYMBOLIC COMPUTATION
Artificial Intelligence
Managing Editor: D. W. Loveland
Editors: S. Amarel A. Biermann L. Bole
A. Bundy H. Gallaire P. Hayes
A. Joshi D. Lenat A. Mackworth
E. Sandewall 1. Siekmann W. Wahlster
Springer Series
SYMBOLIC COMPUTATION -Artificial Intelligence
N.J. Nilsson: Principles of Artificial Intelligence. XV, 476 pages, 139
figs., 1982.
J.H. Siekmann, G. Wrightson (Eds): Automation of Reasoning 1. Clas
sical Papers on Computational Logic 1957-1966. XXII, 525 pages,
1983.
J.H. Siekmann, G. Wrightson (Eds): Automation of Reasoning 2. Clas
sical Papers on Computational Logic 1967-1970. XXII, 638 pages,
1983.
L. Bole (Ed.): The Design ofInterpreters, Compilers, and Editors for
Augmented Transition Networks. XI, 214 pages, 72 figs., 1983.
R.S. Michalski, J.G. Carbonell, T.M. Mitchell (Eds.): Machine Learn
ing. An Artificial Intelligence Approach. 572 pages, 1984.
L. Bole (Ed.): Natural Language Communication with Pictorial Infor
mation Systems. VII, 327 pages, 67 figs., 1984.
J.w. Lloyd: Foundations of Logic Programming. X, 124 pages, 1984.
A. Bundy (Ed.): Catalogue of Artificial Intelligence Tools. XXV, 150
pages, 1984. Second, revised edition, IV, 168 pages, 1986.
M.M. Botvinnik: Computers in Chess. Solving Inexact Problems.
With contributions by A.1. Reznitsky, B.M. Stilman, M.A. Tsfasman,
A.D. Yudin. Translated from the Russian by A.A. Brown. XIV, 158
pages, 48 figs., 1984.
C. Blume, W. Jakob: Programming Languages for Industrial Robots.
XIII, 376 pages, 145 figs., 1986.
L. Bole (Ed.): Natural Language Parsing Systems. XVIII, 367 pages,
155 figs., 1987.
L. Bole (Ed.): Computational Models of Learning. IX, 208 pages, 34
figs., 1987.
N. Cercone, G. McCalla (Eds.): The Knowledge Frontier. Essays in
the Representation of Knowledge. 552 pages, 93 figs., 1987.
G.Rayna: REDUCE. Software for Algebraic Computation. 344 pages,
1987.
D. McDonald, L. Bole (Eds.): Natural Language Generation Systems.
400 pages, 84 figs., 1988.
David D. McDonald Leonard Bole
Editors
Natural Language
Generation Systems
With 84 Illustrations
Springer-Verlag
New York Berlin Heidelberg
London Paris Tokyo
David D. McDonald
Brattle Research Corporation
Cambridge, MA 02138
USA
Leonard Bole
Institute of Informatics, Warsaw
Warsaw University
PKin, pok. 850
PL.OO.901 Warszawa
Poland
Library of Congress Cataloging-in-Publication Data
Natural language generation systems.
(Symbolic computation. Artificial intelligence)
Includes bibliographies.
I. Linguistics-Data processing. l. McDonald,
David D. II. Bolc, Leonard. III. Series.
P98.N295 1988 410'.28'5 87-35649
© 1988 by Springer-Verlag New York Inc.
softcover reprint of the hardcover 1st edition 1988
All rights reserved. This work may not be translated or copied in whole or in part without the written
permission of the publisher (Springer-Verlag, 175 Fifth Avenue, New York, New York 10010, USA),
except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any
form of information storage and retrieval, electronic adaptation, computer software, or by similar or
dissimilar methodology now known or hereafter developed is forbidden.
The use of general descriptive names, trade names, trademarks, etc. in this publication, even if the
former are not especially identified, is not to be taken as a sign that such names, as understood by
the Trade Marks and Merchandise Marks Act, may accordingly be used freely by anyone.
Camera-ready copy provided by the authors.
9 8 7 6 5 432 I
ISBN-13:978-1-4612-8374-4 e-ISBN-13:978-1-4612-3846-1
DOl: 10.1007/978-1-4612-3846-1
Contents
Introduction vii
David D. McDonald
1. Deliberate Writing 1
Richard P. Gabriel
2. Text Generation: The Problem of Text Structure 47
William C. Mann
3. Planning Natural-Language Referring Expressions 69
Douglas E. Appelt
4. Surface Transformations During the Generation of
Written German Sentences 98
Stephan Busemann
5. VIE-GEN: A Generator for German Texts 166
E. Buchberger and H. Horacek
6. Generation of Sentences from a Syntactic Deep Structure
with a Semantic Component 205
Heinz-Dirk Luckhardt
7. Generating Japanese Text from Conceptual Representation 256
Shun Ishizaki
8. Fluency in Natural Language Reports 280
Karen Kukich
9. PHRED: A Generator for Natural Language Interfaces 312
Paul S. Jacobs
vi
10. Generating Language with a Phrasal Lexicon 353
Eduard H. Hovy
Index 385
Contributors 389
Introduction
There was a time when nearly every paper on generatioRstarted out by saying
that "research into natural language generation by computers is in its infancy."
That time is not all that far behind us, and there is not much awareness in the
community at large of what generation really is as a field: What are its issues?
What has been accomplished? The ten papers of this book are a step towards
changing this.
The purpose of this collection has been to give its authors an opportunity to
present their work at much greater length than is available in the usual conference
paper or journal article. As a result, these papers contain details of grammatical
treatments and processing details seldom seen outside of book length mono
graphs. Their topics range from discourse theory, through mechanical transla
tion, to deliberate writing and revision. The authors are also wide ranging
internationally, with contributions from Japan, West Germany, and Austria as
well as the United States.
Natural language generation, as a field, is part of artificial intelligence. It is not
concerned simply with making computers able to print out fluent texts-that is
better done today with simple techniques based on pre-stored text and uncompli
cated substitutions. Instead, it looks ahead to the time when machines will think
complex thoughts and need to communicate them to their human users in a
natural way (natural, at least, for the people who use them). As an engineering
matter then, generation systems supply, the sophisticated knowledge about
natural languages that must come into play when one needs to use wordings that
will overpower techniques based only on symbolic string manipulation tech
niques: intricate patterns of tense and modality, clauses embedded inside com
plex verbs like believe or persuade, complex adjuncts, extended texts that use
pronouns and other reduced forms, and so on. Many of these phenomena can be
coped with by special case techniques for a single application; but when a system
needs to express the same idea from mUltiple perspectives, or to present it to peo
ple with varying backgrounds or goals, a full-fledged generation system, with its
grammar and planning components, must be used.
But like much of artificial intelligence research generally, research on genera
tion is often aimed at purely scientific concerns about the nature of language and
viii
language use in people. As such, it is an unusual cognitive science, since its
methods are "synthetic" rather than "analytical." In AI studies of generation, one
experiments by constructing artifacts-computer programs, observing their
behavior, and comparing it to the behavior of the natural system under study.
This approach has both strengths and weaknesses. Since the computer is the pre
mier medium for the definition and modeling of processes, we can be much more
concrete than other disciplines in modeling psychological phenomena as interact
ing processes. We also have a safeguard on the internal coherence of our theories
that many other disciplines lack since we are forced to have complete, executa
ble, models before our computer programs will run.
At the same time, however, there is the weaknes's that we are continually taking
a leap into the unknown: In order to give our programs something interesting to
work from we are forced to speculate about the nature of thought and intention
the underpinnings of language, and must set our sReculations down in a usable
notation. In this respect our problems in generation are quite different from those
of our sister area in AI, natural language understanding, a difference that is worth
looking at.
When the task is understanding language it is very clear what one is starting
from. Utterances are directly observable, and there is a great body of careful
experimental data from psychology that we can draw on that has characterized
the behavioral characteristics of at least the surface levels of the understanding
process. Note the hedge, "surface levels:' however. Today a person working on
understanding is often seen to make a contribution just by developing a better
parser and stopping with a structural description of a text's syntax. Yet in genera
tion a processing theory that started at as "late" a stage as a structural description
would be considered to have missed the point. In generation one has to come to
grips with a deeper set of questions or one will not be doing serious work. For
instance: How does the form of the conceptual representation that the process
starts with effect the final text and the intermediate processing? Why will one text
be effective and another not be, even though they are very similar in form? And
of course the deepest question: How do we achieve our goals through the use of
language? Just as AI research on generation must venture into the unknown to
posit the representational structures from which its process starts, understanding
research must ultimately do the same to determine the representation of "mean
ing" with which it presumably must end. It is only because it has been acceptable
to stop at an earlier point that people ever imagine understanding research to be
on any firmer ground.
The first paper in this volume, by Richard Gabriel, addresses the problem of
what Gabriel calls "deliberate writing": writing that is "careful and considered"
in contrast with what can happen with spontaneous writing or speech. This task
may well be the ultimate problem for artificial intelligence, since it requires the
strongest imaginable capabilities in planning, knowledge representation,
problem solving, world and person modeling, creativity, and judgement. Gabriel
talks about the qualities of good writing, and of an author's relation to it as origi
nator and reader/reviewer. He lays out the structure and the theory behind his
ix
program, YH, which talks about the algorithms of simple lisp programs. He
emphasises generation as planning, and the revision of plans as an incremental
process driven by critics. The text YH produces (simple descriptions) is strikingly
natural, so much so that the major example has been inserted transparently into
the body of the article and it will be an unusual reader who notices it before they
are told.
Generation has always been concerned with the "discourse structure" that lies
above the individual sentences in isolation that so much oflinguistics has focused
on in the past. William Mann, along with his colleague Sandra Thompson, has for
some time been developing what they call "Rhetorical Structure Theory:' RST is
a structural account of the nature of discourse, characterizing the kind of units
that can make up an extended text and the relationships between them. At the
same time, RST is a functional theory, with structural units characterized accord
ing to the role they play in the forwarding of a speaker's goals. In his paper for this
volume, Mann contrasts RST with the other strong treatinent of extended texts
(specifically paragraph-length monologues) that has been put forward, Kathleen
McKeown's "schemas:'
Doug Appelt's paper, reprinted from the journal Artificial Intelligence, is the
summary of his 1981 dissertation. It presents a logical formalization of the fun
damental notions underlying planning to communicate: goals, intentions, actions
to recruit a listener's aid, actions to indicate a reference, and so on. This formali
zation is then used to drive a non-linear, critic-based planner, KAMP, to produce
complex sentences that are tailored to what the listener does and doesn't know.
KAMP can plan physical actions (e.g., pointing) in coordination with its refer
ences, and has a model of how to achieve multiple goals with a single action.
Stephan Busemann's SUTRA system is the subject of the next paper. Working in
German, SUTRA is designed as a reusable module that works within a larger
generation system. Not specific to anyone domain, it has syntactic knowledge of
constituent placement and multi-clause structure, and morphological knowledge
of case and inflection. As a module, SUTRA is driven from a "verbalized struc
ture" that encodes lexical and case-frame information determined by an earlier
verbalization phase within some companion module. SUTRA was originally deve
loped as part of the HAM-ANS system, but has since been widely used in German
generation efforts. Its strengths lie in a system of transformations that introduce
fluency and naturalness into the form ofthe surface text without burdening the
meaning-centered, earlier generation phases.
VIE-GEN, by Ernst Buchberger and Helmut Horacek, is also a German system.
It is the output "half" of the system VIE-LANG developed at the University of
Vie~a, and shows this through a concern for representing its linguistic (particu
larly lexical) knowledge in a way that allows it to be used for both parsing and
generation. VIE-GEN starts with information given in a structured inheritance
network of semantic primitives, and works in two phases: verbalization and
realization. Words are determined by using discrimination networks to notice
the particular relationships between units, and an intermediate representation
containing word roots and unordered, case-marked constituents mediates
x
between the phases. The system has the capacity to form paraphrasing references
as a variation on using a pronoun, determines the breakdown of a text into
individual sentences deliberately, and has an extensive set of transformations.
Our third paper centered on the German language, by Heinz-Dirk Luckardt,
discusses generation as part of mechanical translation. In this particular design,
part of the Saarbrucken SUSy translation system, the generator is given the bulk
ofthe responsibility for language specific knowledge and operations. Generation
is directed by a language-neutral interlingua structure (used as part of the
EUROTRA european MT project), which is essentially a deep syntactic structure.
The generation procedure is identical for all target languages, with specific lin
guistic facts introduced by parameters. Luckardt discusses the differences in
issues and focus between generation in AI and in MT, and provides an extensive
discussion of the theoretical issues surrounding analyses based on case. The sys
tem uses a dependency grammar and a body oflocal mapping rules that are sensi
tive to a small set of simple semantic features.
The system developed by Shun Ishizaki and described in the next paper could
in some sense be regarded as an MT system, since it is generating stories whose
content is given by parsing English originals. However, it is working from a com
pletely conceptual representation, the first generator of Japanese to do so.
Ishizaki has dealt with a number of features in the Japanese language that have no
direct counter-parts in English, the language most explored in generation; these
include empty subjects marked by particles, strict temporal ordering of events
with coordinated tenses, head-final ordering within phrases, and omission ofNPs
that are modified by subject-relative clauses. The source representation is con
ceptual dependency with MOPs and causal chains organizing larger-scale events.
Karen Kukich's system ANA produces exceptionally facile, paragraph-length
summaries of the day's stock activity on Wall Street; its output is not noticeably
different from what a human news commentator would say. ANA handles the
entire generation process and the conceptualizations underlying it, to the point
of starting from the raw statistics of the day's stock transactions, noticing their
patterns, and determining the appropriate conventional phrases for describing
them. Architecturally, the system consists of a set of production rules that
organize a large phrasal lexicon. Kukich always works with large phrasal units,
never assembling NPs or PPs from smaller concepts, and includes an extensive
discussion of this notion of a "clause-combining;' rather than "sentence building"
grammar. The name of the system, ANA, comes from the psychological term
"anacoluthia;' which is the phenomena of starting an utterance with a phrase that
establishes a syntactic context that can only be finished in a certain way, only to
be unable to complete the utterance because one cannot come up with a continu
ing phrase of the right kind; the system exhibited this behavior early on when its
lexicon was incomplete.
Paul Jacob's PH RED, like ANA, is also a system centered on the use of pre
formed, productive phrases. PH RED was developed at Berkeley as part of the Unix
Consultant System, and designed in such a way that its knowledge of the language
could be directly shared with the parsing system PHRAN. Jacobs breaks down the
