Sentence Planning as Description Using Tree Adjoining Grammar * 
Matthew Stone Christine Doran 
Department of Computer and Information Science Department of Linguistics 
University of Pennsylvania University of Pennsylvania 
Philadelphia, PA 19014 Philadelphia, PA 19014 
matthew@linc, cis. upenn, edu cdoran@linc, cis. upenn, edu 
Abstract 
We present an algorithm for simultaneously 
constructing both the syntax and semantics of 
a sentence using a Lexicalized Tree Adjoin- 
ing Grammar (LTAG). This approach captures 
naturally and elegantly the interaction between 
pragmatic and syntactic constraints on descrip- 
tions in a sentence, and the inferential interac- 
tions between multiple descriptions in a sen- 
tence. At the same time, it exploits linguis- 
tically motivated, declarative specifications of 
the discourse functions of syntactic construc- 
tions to make contextually appropriate syntac- 
tic choices. 
1 Introduction 
Since (Meteer, 1991), researchers in natural language 
generation have recognized the need to refine and re- 
organize content after the rhetorical organization of ar- 
guments and before the syntactic realization of phrases. 
This process has been named sentence planning (Ram- 
bow and Korelsky, 1992). Broadly speaking, it involves 
aggregating content into sentence-sized units, and then 
selecting the lexical and syntactic elements that are used 
in realizing each sentence. Here, we consider this second 
process. 
The challenge lies in integrating constraints from syn- 
tax, semantics and pragmatics. Although most generation 
systems pipeline decisions (Reiter, 1994), we believe the 
most efficient and flexible way to integrate constraints 
in sentence planning is to synchronize the decisions. In 
this paper, we provide a natural framework for dealing 
with interactions and ensuring contextually appropriate 
output in a single pass. As in (Yang et al., 1991), Lex- 
icalized Tree Adjoining Grammar (LTAG) provides an 
*The authors thank Aravind Joshi, Mark Steedman, Martha 
Palmer, Ellen Prince, Owen Rambow, Mike White, Betty 
Birner, and the participants of INLG96 for their helpful com- 
ments on various incarnations of this work. This work has been 
supported by NSF and IRCS graduate fellowships, NSF grant 
NSF-STC SBR 8920230, ARPA grant N00014-94 and ARt 
grant DAAH04-94-G0426. 
abstraction of the combinatorial properties of words. We 
combine LTAG syntax with declarative specifications of 
semantics and pragmatics of words and constructions, so 
that we can build the syntax and semantics of sentences 
simultaneously. To drive this process, we take descrip- 
tion as the paradigm for sentence planning. Our planner, 
SPUD (Sentence Planner Using Descriptions), takes in a 
collection of goals to achieve in describing an event or 
state in the world; SPUD incrementally and recursively 
applies lexical specifications to determine which entities 
to describe and what information to include about them. 
Our system is unique in the streamlined organization of 
the grammar, and in its evaluation both of contextual ap- 
propriateness of pragmatics and of descriptive adequacy 
of semantics. 
The organization of the paper is as follows. In sec- 
tion 2, we review research on generating referring ex- 
pressions and motivate our treatment of sentences as re- 
ferring expressions. Then, in section 3, we present the 
linguistic underpinnings of our work. In section 4, we 
describe our algorithm and its operation on an example. 
Finally, in section 5 we compare our system with related 
approaches. 
2 Sentences as referring expressions 
Our proposal is to treat the realization of sentences as 
parallel to the construction of referring expressions, and 
thereby bring to bear modern discourse-oriented theories 
of semantics and the idea that language use is INTEN- 
TIONAL ACTION. 
Semantically, a DESCRIPTION D is just an open formula. 
D applies to a sequence of entities when substituting them 
for the variables in D yields a true formula. D REFERS to 
C jUSt in case it distinguishes c from its DISTRACTORS-- 
that is D applies to c but to no other salient alternatives. 
Given a sufficiently rich logical language, the meaning 
of a natural language sentence can be represented as a 
description in this sense, by assuming sentences refer to 
entities in a DISCOURSE MODEL, cf. alternative semantics 
(Karttunen and Peters, 1979; Rooth, 1985). 
Pragmatic analyses of referring expressions model 
speakers as PLANNING those expressions to achieve sev- 
eral different kinds of intentions (Donellan, 1966; Appelt, 
198 
1985; Kronfeld, 1986). Given a set of entities to describe 
and a set of intentions to achieve in describing them, a 
plan is constructed by applying operators that enrich the 
content of the description until all intentions are satisfied. 
Recent work on generating definite referring NPs (Reiter, 
1991 ; Dale and Haddock, 1991; Reiter and Dale, 1992; 
Horacek, 1995) has emphasized how circumscribed in- 
stantiations of this procedure can exploit linguistic con- 
text and convention to arrive quickly at short, unambigu- 
ous descriptions. For example, (Reiter and Dale, 1992) 
apply generalizations about the salience of properties of 
objects and conventions about what words make base- 
level attributions to incrementally select words for inclu- 
sion in a description. (Dale and Haddock, 1991) use a 
constraint network to represent the distractors described 
by a complex referring NP, and incrementally select a 
property or relation that rules out as many alternatives as 
possible. Our approach is to extend such NP planning 
procedures to apply to sentences, using TAG syntax and 
a rich semantics. 
Treating sentences as referring expressions allows us 
to encompass the strengths of many disparate proposals. 
Incorporating material into descriptions of a variety of 
entities until the addressee can infer desired conclusions 
allows the sentence planner to enrich input content, so 
that descriptions refer successfully (Dale and Haddock, 
1991) or reduce it, to eliminate redundancy (McDonald, 
1992). Moreover, selecting alternatives on the basis of 
their syntactic, semantic, and pragmatic contributions to 
the sentence using TAG allows the sentence planner to 
choose words in tandem with appropriate syntax (Yang et 
al., 1991), in a flexible order (Elhadad and Robin, 1992), 
and, if necessary, in conventional combinations (Smadja 
and McKeown, 1991; Wanner, 1994). 
3 Linguistic Specifications 
Realizing this procedure requires a declarative specifica- 
tion of three kinds of information: first, what operators 
are available and how they may combine; second, how 
operators specify the content of a description; and third, 
how operators achieve pragmatic effects. We represent 
operators as elementary trees in LTAG, and use TAG op- 
erations to combine them; we give the meaning of each 
tree as a formula in an ontologically promiscuous rep- 
resentation language; and, we model the pragmatics of 
operators by associating with each tree a set of discourse 
constraints describing when that operator can and should 
be used. 
Other frameworks have the capability to make com- 
parable specifications; for example, HPSG (Pollard and 
Sag, 1994) feature structures describe syntax (SUBCAT), 
semantics (CONTI3NT) and pragmatics (CONTEXT). We 
choose TAG because it enables local specification of syn- 
tactic dependencies in explicit constructions and flexibil- 
ity in incorporating modifiers; further, it is a constrained 
grammar formalism with tractable computational proper- 
ties. 
3.1 Syntactic specification 
TAG (Joshi et al., 1975) is a grammar formalism built 
around two operations that combine pairs of trees, SUB- 
STITUTION and ADJOINING. A TAG grammar consists of 
a finite set of ELEMENTARY trees, which can be combined 
by these substitution and adjoining operations to produce 
derived trees recognized by the grammar. In substitu- 
tion, the root of the first tree is identified with a leaf of the 
second tree, called the substitution site. Adjoining is a 
more complicated splicing operation, where the first tree 
replaces the subtree of the second tree rooted at a node 
called the adjunction site; that subtree is then substituted 
back into the first tree at a distinguished leaf called the 
FOOT node. Elementary trees without foot nodes are 
called INITIAL trees and can only substitute; trees with 
foot nodes are called AUXILIARY trees, and must adjoin. 
(The symbol $ marks substitution sites, and the symbol 
* marks the foot node.) Figure l(a) shows an initial tree 
representing the book. Figure l(b) shows an auxiliary 
tree representing the modifier syntax, which could adjoin 
into the tree for the book to give the syntax book. 
Our grammar incorporates two additional principles. 
First, the grammar is LEXICALIZED (Schabes, 1990): each 
elementary structure in the grammar contains at least 
one lexical item. Second, our trees include FEATURES, 
following (Vijay-Shanker, 1987). 
LTAG elementary trees abstract the combinatorial 
properties of words in a linguistically appealing way. All 
predicate-argument structures are localized within a sin- 
gle elementary tree, even in long-distance relationships, 
so elementary trees give a natural domain of locality 
over which to state semantic and pragmatic constraints. 
The LTAG formalism does not dictate particular syntactic 
analyses; ours follow basic GB conventions. 
3.2 Semantics 
We specify the semantics of trees by applying two prin- 
ciples to the LTAG formalism. First, we adopt an ONTO- 
LOGICALLY PROMISCUOUS representation (Hobbs, 1985) 
that includes a wide variety of types of entities. On- 
tological promiscuity offers a simple syntax-semantics 
interface. The meaning of a tree is just the CONJUNCTION 
of the meanings of the elementary trees used to derive it, 
once appropriate parameters are recovered. Such fiat se- 
mantics is enjoying a resurgence in NLP; see (Copestake 
et al., 1997) for an overview and formalism. Second, 
we constrain these parameters syntactically, by labeling 
each syntactic node as supplying information about a par- 
ticular entity or collection of entities, as in Jackendoff's 
X-bar semantics (Jackendoff, 1990). A node X:X (about 
×) can only substitute or adjoin into another node with the 
same label. These semantic parameters are instantiated 
using a knowledge base (cf. figure 7). 
For Jackendoff, noun phrases describe ordinary indi- 
viduals, while PPs describe PLACES or PATHS and VPs 
describe ACTIONS and EVENTUALITIES (in terms of a Re- 
ichenbachian reference point). Under these assumptions, 
the trees of figure 1, are elaborated for semantics as in 
199 
S 
N 
NP, s 
DetP N /~ 
\[ I N N* NPJ, VP 
D book /~ 
V NP 
I I the (h) 
(a) have ¢ 
(c) 
Figure 1: Sample LTAG trees: (a) NP, (b) Noun-Noun Compound, (c) Topicalized Transitive 
NP : <l>x 
DetP N : <1> \[ , 
Det book 
I 
the 
book(x) (a) 
N : <l>x 
N : syntax N* : <1> 
t 
syntax 
concerns(x, syntax) 
(b) 
S : <l><r,havlng> 
NPJ. : <2>havee S : <1> 
NP;. : hayer VP: <1> 
V NP : <2> I I 
/have/ t 
during(r, having) ^ have(having, hayer, havee) 
(c) 
Figure 2: LTAG trees with semantic specifications 
figure 2. Ontological promiscuity makes it possible to 
explore more complicated analyses in this general frame- 
work. For example, in (Stone and Doran, 1996), we use 
reference to properties, actions and belief contexts (Bal- 
lim et al., 1991) to describe semantic collocations (Puste- 
jovsky, 1991) and idiomatic composition (Nunberg et al., 
1994). 
3.3 Pragmatics 
Different constructions make different assumptions about 
the status of entities and propositions in the discourse, 
which we model by including in each tree a specification 
of the contextual conditions under which use of the tree 
is pragmatically licensed. We have selected four repre- 
sentative pragmatic distinctions for our implementation; 
however, the framework does not commit one to the use 
of particular theories. 
We use the following distinctions. First, entities differ 
in NEWNESS (Prince, 1981). At any point, an entity is ei- 
ther new or old to the HEARER and either new or old to the 
DISCOURSE. Second, entities differ in SALIENCE (Grosz 
and Sidner, 1986; Grosz et al., 1995). Salience assigns 
each entity a position in a partial order that indicates 
how accessible it is for reference in the current con- 
text. Third, entities are related by salient PARTIALLY- 
ORDERED SET (POSET) RELATIONS to other entities in the 
context (Hirschberg, 1985). These relations include part 
and whole, subset and superset, and membership in a 
common class. Finally, the discourse may distinguish 
some OPEN PROPOSITIONS (propositions containing free 
variables) as being under discussion (Prince, 1986). We 
assume that information of these four kinds is available 
in a model of the current discourse state. 
The applicability conditions of constructions can freely 
make reference to this information. In particular, NP trees 
include the determiner (the determiner does not have a 
separate tree), the head noun, and pragmatic conditions 
that match the determiner with the status of the entity 
in context, as in 3(a). Following (Gundel et al., 1993), 
the definite article the may be used when the entity is 
UNIQUELY IDENTIFIABLE in the discourse model, i.e. the 
hearer knows or can infer the existence of this entity and 
can distinguish it from any other hearer-old entity of equal 
or greater salience. (Note that this test only determines 
the status of the entity in context; we ensure separately 
that the sentence includes enough content to distinguish 
200 
NP : <l>x 
N : <I>x 
DetP N : ,:1> / ~ - \ 
N : syntax N* I 
Det book 
I syntax 
the concerns(x, syntax) \[always applicable\] 
book(x) (b) (unique-id(x)) 
(a) 
: <1> 
S : <l><r, having> 
NP~ :<2>havee S : <1> 
NP,L : hayer VP: <1> 
V NP : <2> 
/have/ E 
during(r, having) ^ have(having, hayer, havee) 
(in-poset(havee), in-op(have(having, haver, havee))) 
(c) 
Figure 3: LTAG trees with semantic and pragmatic specifications 
the entity from all its alternatives.) In contrast, the indef- 
inite articles, a, an, and 0, are used for entities that are 
NOT uniquely identifiable. 
S trees specify the main verb and the number and po- 
sition of its arguments. Our S trees specify the unmarked 
SVO order or one of a number of fancy variants: topical- 
ization (TOP), left-dislocation (LD), and locative inversion 
(INV). We follow the analysis of TOP in (Ward, 1985). 
For Ward, TOP is not a topic-marking construction at all. 
Rather, TOP is felicitous as long as (1) the fronted NP is in 
a salient poset relation to the previous discourse and (2) 
the utterance conveys a salient open proposition which 
is formed by replacing the tonically stressed constituent 
with a variable (3(c)). Likewise, we follow (Prince, 1993) 
and (Birner, 1992) for LD and INV respectively. 
4 SPUD 
4.1 The algorithm 
Our system takes two types of goals. First, goals of 
the form distinguish x as cat instruct the algorithm to 
construct a description of entity x using the syntactic 
category cat. Ifx is uniquely identifiable in the discourse 
model, then this goal is only satisfied when the meaning 
planned so far distinguishes x for the hearer. Ifx is hearer 
new, this goal is satisfied by including any constituent 
of type cat. Second, goals of the form communicate p 
instruct the algorithm to include the proposition p. This 
goal is satisfied as long as the sentence IMPLIES p given 
shared common-sense knowledge. 
In each iteration, our algorithm must determine the ap- 
propriate elementary tree to incorporate into the current 
description. It performs this task in two steps to take 
advantage of the regular associations between words and 
trees in the lexicon. Sample lexical entries are shown 
in figure 4. They associate a word with the semantics 
of the word, special pragmatic restrictions on the use of 
the word, and a set of trees that describe the combina- 
tory possibilities for realizing the word and may impose 
additional pragmatic restrictions. Tree types are shared 
between lexical items (figure 5). This allows us to spec- 
ify the pragmatic constraints associated with the tree type 
once, regardless of which verb selects it. Moreover, we 
can determine which tree to use by looking at each tree 
ONCE per instantiation of its arguments, even when the 
same tree is associated with multiple lexical items. 
Hence, the first step is to identify applicable lexical 
entries by meaning: these items must truly and appropri- 
ately describe some entity; they must anchor trees that can 
substitute or adjoin into a node that describes the entity; 
and they must distinguish entities from their distractors 
or entail required information. Then, the second step 
identifies which of the associated trees are applicable, 
by testing their pragmatic conditions against the current 
representation of discourse. The algorithm identifies the 
combinations of words and trees that satisfy the most 
communicate goals and eliminate the most distractors. 
From these, it selects the entry with the most specific se- 
mantic and pragmatic licensing conditions. This means 
that the algorithm generates the most marked licensed 
form. In (Stone and Doran, 1996) we explore the use 
of additional factors, such as attentional state and lexical 
preferences, in this step. 
The new tree is then substituted or adjoined into the 
existing tree at the appropriate node. The entry may 
specify additional goals, because it describes one entity 
in terms of a new one. These new goals are added to the 
current goals, and then the algorithm repeats. 
Note that this algorithm performs greedy search. To 
avoid backtracking, we choose uninflected forms. Mor- 
phological features are set wherever possible as a result 
of the general unification processes in the grammar; the 
inflected form is determined from the lemma and its as- 
sociated features in a post-processing step. 
The specification of this algorithm is summarized in 
the following pseudocode: 
201 
STEM 
/buy/ 
/sell/ 
/purchase/ 
/book/ 
SEMANTICS S YNTAX PRAGMATICS 
S buyer/buy/bought/from/seller, etc. register(informal) 
S seller~sell~bought~to~buyer, etc. register(informal) 
S buyer/purchase/bought/from/seller, etc. register(formal) 
book(x) /a//book/, etc. \[always possible\] 
S = buy(buying,buyer, seller, bought) 
Figure 4: Sample entries from the lexicon 
SUBCAT FRAME TREES PRAGMATICS 
Intransitive Active \[always possible\] 
Transitive Active \[always possible\] 
Topicalized Object in-poset(obj), in-op(event) 
Left-Dislocated Object in-poset(obj) 
Ditransitive Active \[always possible\] 
Topicalized Dir Object in-poset(dir obj), in-op(event) 
Left-Dislocated Dir Object in-poset(dir obj) 
PP Predicative Active \[always possible\] 
Locative Inversion newer-than(subj,loc) 
etc. 
Figure 5: Sample entries from the tree database 
until goals are satisfied: 
determine which uninflected forms apply; 
determine which associated trees apply; 
evaluate progress towards goals; 
incorporate most specific, best ( form, tree ): 
perform adjunction or substitution; 
conjoin new semantics; 
add any additional goals; 
4.2 The system 
SPUD's grammar currently includes a range of syntactic 
constructions, including adjective and PP modification, 
relative clauses, idioms and various verbal alternations. 
Each is associated with a semantic and pragmatic specifi- 
cation as discussed above and illustrated in figures 4 and 
5. These linguistic specifications can apply across many 
domains. 
In each domain, an extensive set of inferences, pre- 
sumed known in common with the user, are required to 
ensure appropriate behavior. We use logic programming 
to capture these inferences. In our domain, the system 
has the role of a librarian answering patrons' queries. 
Our specifications define: the properties and identities of 
objects (e.g., attributes of books, parts of the library); the 
taxonomic relationships among terms (e.g., that a service 
desk is an area but not a room); and the typical span and 
course of events in the domain (e.g., rules about how 
to check out books). This information is complete and 
available for each lexical entry. Of course, SPUD also 
represents its private knowledge about the domain. This 
includes facts like the status of books in the library. 
4.3 An example 
Suppose our system is given the task of answering the 
following question: 
(1) Do you have the books for Syntax 551 and 
Pragmatics 590? 
Figure 6 shows part of the discourse model after process- 
ing the question. The two books, the set they comprise 
(introducing a poset relation), and the library are men- 
tioned in (1). Hence, these entities must be both hearer- 
old and discourse-old. As in centering (Grosz et al., 
1995), the subject is taken to be the most salient entity. 
Finally, the meaning of the question becomes a salient 
open proposition. 
On the basis of the knowledge in figure 7, a rhetor- 
ical planner might decide to answer by describing state 
have27 as an S and lose5 likewise. To construct its refer- 
ence to have27, SPUD first determines which lexical and 
syntactic options are available. Using the lexicon and in- 
formation about have27 available from figure 7(b), SPUD 
determines that, of lemmas that truthfully and appropri- 
ately describe have27 as an S,/have/has the most spe- 
cific licensing conditions. The tree set for/have/includes 
unmarked, LD and TOP trees. All are licensed, because 
of the poset relation R between book19 and books and 
the salient open proposition O. We choose TOP, the tree 
with the most specific condition--TOP requires R and O, 
while LD requires only R and the unmarked form has no 
requirements. 
Thus, a topicalized /have/ tree, appropriately instan- 
tiated as shown in figure 8, is added to the description. 
The tree refers to three new entities, the object book19, 
the subject library and the reference point r of the tense. 
202 
STATUS 
DISCOURSE OLD: 
HEARER OLD: 
SALIENCE: 
POSET RELATIONS: 
" OPEN PROPOSITION: 
ENTITIES 
book19, book2, books, library, patron 
bookl 9, book2, books, library, patron 
{library, patron} > { bookl 9, book2, books} 
book19 MEMBER-OF books; book2 MEMBER-OF books 
library X have Y: X={does/doesn't}; Y E books. 
Figure 6: Discourse model for the example 
book(book19) book(book2) 
concerns(book19, syntax) concerns(book2, pragrnatics) 
(a) Common Knowledge 
have(have27, library, book19) lost(lose5, book2) 
during (have27, now) during(lose5, now) 
(b) Speaker's Knowledge 
Figure 7: Knowledge bases for the example 
S : <1><r,have27> 
NP.~ : <2>book19 S : <1> 
NP,~ : library VP :<1> 
V NP : <2> 
I I 
/have/ 
during(r, have27) A have(have27, library, book19) 
Figure 8: The first tree incorporated into the description 
of have27. 
Subgoals of distinguishing these entities are introduced. 
Other constructions that describe one entity in terms of 
another, such as complex NPs, relative clauses and se- 
mantic collocations, are also handled this way by SPUD. 
The algorithm now selects the goal of describing 
book19 as an NP. Again, the knowledge base is con- 
sulted to select NP lemmas that truly describe book19. 
The best is/book/. (Note that SPUD would use it if either 
the verb or the discourse context ruled out all distrac- 
tors.) The tree set for/book/includes trees with definite 
and indefinite determiners; since the hearer can uniquely 
identify book19, the definite tree is selected and substi- 
tuted as the leftmost NP, as in figure 9. 
The goal of distinguishing book19 is still not satis- 
fied, however; as far as the hearer knows, both book19 
and book2 could match the current description. We con- 
sult the knowledge base for further information about 
book19. The modifier entry for the lexical item/syntax/ 
can apply to the new N node; its tree adjoins there, giving 
the tree in figure 10. Note that because trees are lexi- 
calized and instantiated, and must unify with the existing 
derivation, SPUD can enforce collocations and idiomatic 
S : <1><r,have27> 
NP : <2>book19 S : <1> 
DetP N :<2> NPJ. : library VP :<1> 
Det book V ~ : <2> 
I I r 
the /have/ ¢ 
during(r, have27) A 
have(have27, library, book19) A book(book19) 
Figure 9: The description of have27 after substituting 
the book. 
S : <l><r,have27> 
: <2>book19 S : <1> 
DetP N : <2> NP~ : library VP : <1> 
Det N :syntax N :<2> V NP :<2> 
I I t I t 
the syntax book /have/ c 
during(r, have27) A have(have27, library, book19) A 
book(book19) A concerns(book19, syntax) 
Figure 10: The tree with the complete description of 
book19. 
composition in steps like this one. 
Now we describe the library. Since we ARE the library, 
the lexical item/we/is chosen. Finally, we describe 
r. Consulting the knowledge base, we determine that 
r is now, and that the present tense morpheme applies. 
For uniformity with auxiliary verbs, we represent it as a 
separate tree, in this case with a null head, which assigns 
a morphological feature to the main verb. This gives the 
203 
$ : <1><r,ha~27~ 
NP : ,~1> boolO0 
I~¢P N , <2> 
D®I N : syntax N : <2> 
t!e syntax blook 
s : ,~1> 
/h ve/ 
during(r, have27) A have(have27, library, book19) A 
book(book19) ^ concerns(book19, syntax) A 
we(library) A pres(r, have27) 
Figure 11: The final tree. 
tree in figure 11, representing the sentence: 
(2) The syntax book, we have. 
All goals are now satisfied. Note that the semantics has 
been accumulated incrementally and straightforwardly in 
parallel with the syntax. 
To illustrate the role of inclusion goals, let us suppose 
that the system also knows that book19 is on reserve 
in the state have27. Given the additional input goal of 
communicating this fact, the algorithm would proceed as 
before, deriving The syntax book we have. However, the 
new goal would still be unsatisfied; in the next iteration, 
the PP on reserve would be adjoined into the tree to satisfy 
it: The syntax book, we have on reserve. Because TAG 
allows adjunction to apply at any time, flexible realization 
of content is facilitated without need for sophisticated 
back-tracking (Elhadad and Robin, 1992). 
The processing of this example may seem simple, but 
it illustrates the way in which SPUD integrates syntac- 
tic, semantic and pragmatic knowledge in realizing sen- 
tences. We tackle additional examples in (Stone and 
Doran, 1996). 
5 Comparison with related work 
The strength of the present work is that it captures a num- 
ber of phenomena discussed elsewhere separately, and 
does so within a unified framework. With its incremental 
choices and its emphasis on the consequences of func- 
tional choices in the grammar, our algorithm resembles 
the networks of systemic grammar (Mathiessen, 1983; 
Yang et al., 1991). However, unlike systemic networks, 
our system derives its functional choices dynamically us- 
ing a simple declarative specification of function. Like 
many sentence planners, we assume that there is a flexible 
• association between the content input to a sentence plan- 
ner and the meaning that comes out. Other researchers 
(Nicolov et al., 1995; Rubinoff, 1992) have assumed that 
this flexibility comes from a mismatch between input 
content and grammatical options. In our system, such 
differences arise from the referential requirements and 
inferential opportunities that are encountered. 
Previous authors (McDonald and Pustejovsky, 1985; 
Joshi, 1987) have noted that TAG has many advantages 
for generation as a syntactic formalism, because of its 
localization of argument structure. (Joshi, 1987) states 
that adjunction is a powerful tool for elaborating descrip- 
tions. These aspects of TAGs are crucial to SPUD, as they 
are to (McDonald and Pustejovsky, 1985; Joshi, 1987; 
Yang et al., 1991; Nicolov et al., 1995; Wahlster et al., 
1991; Danlos, 1996). What sets SPUD apart is its simul- 
taneous construction of syntax and semantics, and the 
tripartite, lexicalized, declarative grammatical specifica- 
tions for constructions it uses. Two contrasts should be 
emphasized in this regard. (Shieber et al., 1990; Shieber 
and Schabes, 1991) construct a simultaneous derivation 
of syntax and semantics but they do not construct the 
semantics--it is an input to their system. (Prevost and 
Steedman, 1993; Hoffman, 1994) represent syntax, se- 
mantics and pragmatics in a lexicalized framework, but 
concentrate on information structure rather than the prag- 
matics of particular constructions. 
6 Conclusion 
Most generation systems pipeline pragmatic, semantic, 
lexical and syntactic decisions (Reiter, 1994). With the 
fight formalism, constructing pragmatics, semantics and 
syntax simultaneously is easier and better. The approach 
elegantly captures the interaction between pragmatic and 
syntactic constraints on descriptions in a sentence, and the 
inferential interactions between multiple descriptions in a 
sentence. At the same time, it exploits linguistically mo- 
tivated, declarative specifications of the discourse func- 
tions of syntactic constructions to make contextually ap- 
propriate syntactic choices. 

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