A PARAMETERIZED APPROACH TO INTEGRATING ASPECT 
WITH LEXICAL-SEMANTICS FOR MACHINE TRANSLATION 
Bonnie J. Dorr* 
Institute for Advanced Computer Studies 
A.V. Williams Building 
University of Maryland 
College Park, MD 20742 
bonnie@umiacs.umd.edu 
ABSTRACT 
This paper discusses how a two-level knowledge rep- 
resentation model for machine translation integrates as- 
pectual information with lexical-semantic information by 
means of parameterization. The integration of aspect 
with lexical-semantics is especially critical in machine 
translation because of the lexical selection and aspec- 
tual realization processes that operate during the pro- 
duction of the target-language sentence: there are of- 
ten a large number of lexical and aspectual possibili- 
ties to choose from in the production of a sentence from 
a lexical semantic representation. Aspectual informa- 
tion from the source-language sentence constrains the 
choice of target-language terms. In turn, the target- 
language terms limit the possibilities for generation of 
aspect. Thus, there is a two-way communication chan- 
nel between the two processes. This paper will show 
that the selection/realization processes may be parame- 
terized so that they operate uniformly across more than 
one language and it will describe how the parameter- 
based approach is currently being used as the basis for 
extraction of aspectual information from corpora. 
INTRODUCTION 
This paper discusses how the two-level knowledge 
representation model for machine translation presented 
by Dorr (1991) integrates aspectual information with 
lexical-semantic information by means of parameteriza- 
tion. The parameter-based approach borrows certain 
ideas from previous work such as the lexical-semantic 
model of Jackendoff (1983, 1990) and models of as- 
pectual representation including Bach (1986), Comrie 
(1976), Dowty (1979), Mourelatos (1981), Passonneau 
(1988), Pustejovsky (1988, 1989, 1991), and Vendler 
(1967). However, unlike previous work, the current 
approach examines aspectual considerations within the 
context of machine translation. More recently, Bennett 
*This paper describes research done in the Institute for 
Advanced Computer Studies at the University of Maryland. 
A special thanks goes to Terry Gaasterland and Ki Lee for 
helping to close the gap between properties of aspectual in- 
formation and properties of lexical-semantic structure. In 
addition, useful guidance and commentary during this re- 
search were provided by Bruce Dawson, Michael Herweg, 
Jorge Lobo, Paola Merlo, Norbert Hornstein, Patrick Saint- 
Dizier, Clare Voss, and Amy Weinberg. 
(1) Syntactic: 
(a) Null Subject divergence: 
E: I have seen Mary 4. S: He vlsto a Marls 
(Have seen (to) Mary) 
(b) Constituent Order divergence, 
E: I have seen Mary 4. G: Ich habe Marie gesehen 
(I have Mar~" seen) 
(2) Lexicel-Semantic: 
(a) Thematic divergence: 
E: I like Mary 4. $: Marls me gusts a mf (Mary pleases me) 
(b) Structural divergence: 
E: John entered the house 4. S: Juan entr6 en la cas& 
(John entered in the house) 
(c) Cat esorlal divergence: 
E: Yo ten~o hambre 4* S: Ich habe Hun~er (I have hun~er) 
(3) Aepectuah 
(a) lterative Divergence: 
E: John stabbed Mary 4. 
S: Juan le dio una puflaJada a Marls 
(John gave a knife-wound to Mary) 
S: Juan le dio pufialadas a Marls 
(John gave knife-wounds to Mary) 
(b) Duratlve Divergence, 
E: John met/knew Mary 4* 
S: Juan coaoci6 a Marls (John met Mary) 
S: Juan conoci£ a M&rfa (John knew Merit) 
Figure 1: Three Levels of MT Divergences 
et el. (1990) have examined aspect and verb semantics 
within the context of machine translation in the spirit 
of Moens and Steedman (1988). This paper borrows 
from, and extends, these ideas by demonstrating how 
this theoretical framework might be adapted for cross- 
linguistic applicability. The framework has been tested 
within the context of an interlingual machine transla- 
tion system and is currently being used as the basis for 
extraction of aspectual information from corpora. 
The integration of aspect with lexical-semantics is es- 
pecially critical in machine translation because of the 
lexical selection and aspectual realization processes that 
operate during the production of the target-language 
sentence: there are often a large number of lexical and 
aspectual possibilities to choose from in the production 
of a sentence from a lexical semantic representation. As- 
pectual information from the source-language sentence 
constrains the choice of target-language terms. In turn, 
the target-language terms limit the possibilities for gen- 
eration of aspect. Thus, there is a two-way communica- 
tion channel between the two processes. 
Figure 1 shows some of the types of parametric diver- 
9ences (Dorr, 1990a) that can arise cross-linguistically. 
257 
We will focus primarily on the third type, aspectual dis- 
tinctions, and show how these may be discovered through 
the extraction of information in a monolingual corpus. 
We adopt the viewpoint that the algorithms for extrac- 
tion of syntactic, lexical-semantic, and aspectual infor- 
mation must be well-grounded in linguistic theory. Once 
the information is extracted, it may then be used as the 
basis of parameterized machine translation. Note that 
we reject the commonly held assumption that the use of 
corpora necessarily suggests that statistical or example- 
based techniques be used as the basis for a machine 
translation system. 
The following section discusses how the two levels of 
knowledge, aspectual and lexical-semantic, are used in 
an interlingual model of machine translation. We then 
describe how this information may be parameterized. Fi- 
nally, we discuss how the automatic acquisition of new 
lexical entries from corpora is achieved within this frame- 
work. 
TWO-LEVEL Kit MODEL: ASPECTUAL 
AND LEXICAL-SEMANTIC KNOWLEDGE 
The hypothesis proposed by Tenny (1987, 1989) is 
that the mapping between cognitive structure and syn- 
tactic structure is governed by aspectual properties. 
The implication is that lexical-semantic knowledge ex- 
ists at a level that does not include aspectual infor- 
mation (though these two types of knowledge may de- 
pend on each other in some way). This hypothesis 
is consistent with the view adopted here: we assume 
that lexical semantic knowledge consists of such notions 
as predicate-argument structure, well-formedness condi- 
tions on predicate-argument structures, and procedures 
for lexical selection of surface-sentence tokens; all other 
types of knowledge must be represented at some other 
level. 
Figure 2 shows the overall design of the UNITRAN 
machine translation system (Dorr, 1990a, 1990b). The 
system includes a two-level model of knowledge represen- 
tation (KR) (see figure 2(a)) in the spirit of Dorr (1991). 
The translation example shown here illustrates the fact 
that the English sentence John went to the store when 
Mary arrived can be translated in two ways in Spanish. 
This example will be revisited later. 
The lexical-semantic representation that is used as the 
interlingua for this system is an extended version of lexi. 
cal conceptual structure (henceforth, LCS) (see Jackend- 
off (1983, 1990)). This representation is the basis for the 
lexical-semantic level that is included in the KR compo- 
nent. The second level that is included in this component 
is the aspectual structure. 
The KR component is parameterized by means of se- 
lection charts and coercion functions. The notion of se- 
lection charts is described in detail in Dorr and Gaaster- 
land (submitted) and will be discussed in the context 
of machine translation in the section on the Selection 
of Temporal Connectives. The notion of coercion func- 
tions was introduced for English verbs by Bennett et al. 
(1990). We extend this work by parameterizing the coer- 
cion functions and setting the parameters to cover Span- 
ish; this will be discussed in the section on Selection and 
(~) 
(b) 
I Lexical- Semantic 
Structure 
I Aspectual Structure 
I 
Syntactic 
Structure 
Selection ~nd 
Coercion P&r&meters 
for English 
Selection and 
Coercion P~r~meters 
for Spanish 
John went to the store 
when Mary •rrived 
Juan fue 8 Is tiend• ..~ 
cu•ndo M•rf• lleg6 
-4~ Ju•n fue • 18 fiend& 
81 llegar Marf• 
Figure 2: Overall Design of UNITRAN 
Aspectual Realization of Verbs. 
An example of the type of coercion that will be con- 
sidered in this paper is the use of durative adverbials: 
{ foranhour. } (4) (i) John ransacked the house until Jack arrived. 
{ foranhour. } (ii) John destroyed the house until Jack arrived. 
(iii), John obliterated the house{ for an hour.until Jack arrived. } 
Durative adverbials (e.g., for an hour and until...) 
are viewed as anti-cuiminators (following Bennett et al. 
(1990)) in that they change the main verb from an ac- 
tion that has a definite moment of completion to an ac- 
tion that has been stopped but not necessarily finished. 
For example, the verb ransack is allowed to be modified 
by a durative adverbial since it is inherently durative; 
thus, no coercion is necessary in order to use this verb 
in the durative sense. In contrast, the verb destroy is 
inherently non-durative, but it is coerced into a durative 
action by means of adverbial modification; this accounts 
for the acceptability of sentence (4)(ii). 1 The verb oblit- 
erate must necessarily be non-durative (i.e., it is inher- 
ently non-durative and non-coercible), thus accounting 
for the ill-formedness of sentence (4)(iii). 
In addition to the KR component, there is also a syn- 
tactic representation (SR) component (see figure 2(b)) 
that is used for manipulating the syntactic structure of 
a sentence. We will omit the discussion of the SR compo- 
nent of UNITRAN (see, for example, Dorr (1987)) and 
will concern ourselves only with the KR component for 
the purposes of this paper. 
The remainder of this section defines the dividing line 
between lexical knowledge (i.e., properties of predicates 
1 Some native speakers consider sentence (4)(ii) to be odd, 
at best. This is additional evidence for the existence of in- 
herent features and suggests that, in some cases (i.e., for 
some native speakers), the inherent features are considered 
to be absolute overrides, even in the presence of modifiers 
that might potentially change the aspectual features. 
258 
and their arguments) and non-lexical knowledge (i.e., 
aspect), and discusses how these two types of knowledge 
are combined in the Kit component. 
Lexlcal-Semantic Structure. Lexical-semantic struct- ure exists at a level of knowledge representation that 
is distinct from that of aspect in that it encodes infor- 
mation about predicates and their arguments, plus the 
potential realization possibilities in a given language. 
In terms of the representation proposed by Jackendoff 
(1983, 1990), the lexical-semantic structures for the two 
events of figure 2 would be the following: 
(5) (i) \[Event GOLoc (\[Thing 
John\], 
\[Position TOboc (\[Thing John\], \[Location Storel)l)\] 
(ii) \[Event GOLoc (\[Thin s 
Mary\], 
\[Position TOLoc (\[Thing Mary\], \[Location el)\])\] 2 
Although temporal connectives are not included in Jack- 
endoff's theory, it is assumed that these two structures 
would be related by means of a lexical-semantic token 
corresponding to the temporal relation between the two 
events. 
The lexical-semantic representation provided by Jack- 
endoff distinguishes between events and states; however, 
this distinction alone is not sufficient for choosing among 
similar predicates that occur in different aspectual cat- 
egories. In particular, events can be further subdivided 
into more specific types so that non-cnlminative events 
(i.e., events that do not have a definite moment of com- 
pletion) such as ransack can be distinguished from cul- 
minative events (i.e., events that have a definite moment 
of completion) such as obliterate. This is a crucial dis- 
tinction given that these two similar words cannot be 
used interchangeably in all contexts. Such distinctions 
are handled by augmenting the lexical-semantic frame- 
work so that it includes aspectual information, which we 
will describe in the next section. 
Aspectual Structure. Aspect is taken to have two 
components, one comprised of inherent features (i.e., 
those features that distinguish between states and 
events) and another comprised of non-inherent features 
(i. e., those features that define the perspective, e.g., sim- 
ple, progressive, and perfective). This paper will focus 
primarily on inherent features, z 
Previous representational frameworks have omitted 
aspectual distinctions among verbs, and have typically 
merged events under the single heading of dynamic (see, 
e.g., Yip (1985)). However, a number of aspectually 
oriented lexical-semantic representations have been pro- 
posed that more readily accommodate the types of as- 
pectual distinctions discussed here. The current work 
borrows extends these ideas for the development of an 
interlingual representation. For example, Dowty (1979) 
and Vendler (1967) have proposed a four-way aspectual 
classification system for verbs: states, activities, achieve- 
ments, and accomplishments, each of which has a dif- 
ferent degree of telicity (i.e., culminated vs. nonculmi- 
2The empty location denoted by e corresponds to an un- 
realized argument of the predicate arrive. 
aSee Dorr and Gaasterland (submitted) for a discussion 
about non-inherent aspectua\] features. 
nated), and/or atomicity (i.e., point vs. extended). 4 A 
similar scheme has been suggested by Bach (1986) and 
Pustejovsky (1989) (following Mourelatos (1981) and 
Comrie (1976)) in which actions are classified into states, 
processes, and events. 
The lexical-semantic structure adopted for UNITRAN 
is an augmented form of Jackendoff's representation 
in which events are distinguished from states (as be- 
fore), but events are further subdivided into activities, 
achievements, and accomplishments. The subdivision is 
achieved by means of three features proposed by Ben- 
nett etal. (1990) following the framework of Moens and 
Steedman (1988): -t-dynamic (i.e., events vs. states, 
as in the Jackendoff framework), +telic (i.e., culmina- 
tive events (transitions) vs. noneulminative events (ac- 
tivities)), and -I-atomic (i.e., point events vs. extended 
events). We impose this system of features on top of 
the current lexical-semantic framework. For example, 
the lexical entry for all three verbs, ransack, obliterate, 
and destroy, would contain the following lexical-semantic 
representation: 
(6) \[Event CAUSE (\[Thing X\], \[Event GOLoc 
(\[Thing X\], \[Position 
TOLoc (\[X John\], \[Property DESTROYED\])\])\])\] 
The three verbs would then be distinguished by annotat- 
ing this representation with the aspectual features \[+d,- 
t,-a\] for the verb ransack, \[+d,+t,-a\] for the verb destroy, 
and \[+d,+t,+a\] for the verb obliterate, thus providing 
the appropriate distinction for cases such as (4). 5 
In the next section, we will see how the lexical- 
semantic representation and the aspeetual structure are 
combined parametrically to provide the framework for 
generating a target-language surface form. 
CROSS-LINGUISTIC APPLICABILITY: 
PARAMETERIZATION OF THE 
TWO-LEVEL MODEL 
Although issues concerning lexical-semantics and as- 
pect have been studied extensively, they have not been 
examined sufficiently in the context of machine trans- 
lation. Machine translation provides an appropriate 
testbed for trying out theories of lexical semantics and 
aspect. The problem of lexical selection during genera- 
tion of the target language is the most crucial issue in 
this regard. The current framework facilitates the se- 
lection of temporal connectives and the aspectual real- 
ization of verbs. We will discuss each of these, in turn, 
4Dowty's version of this classification collapses achieve- 
ments and accomplishments into a single event type called 
a transition, which covers both the point and extended ver- 
sions of the event type. The rationale for this move is that 
all events have some duration, even in the case of so-called 
punctual events, depending on the granulaxity of time in- 
volved. (See Passonneau (1988) for an adaptation of this 
scheme as implemented in the PUNDIT system.) For the 
purposes of this discussion, we will maintain the distinction 
between achievements and accomplishments. 
5This system identifies five distinct categories of predi- 
State: i-d\] (llke, know) Activity (point): i-t-d, -t, -I-a\] (tap, wink) 
cates: Activity (extended): i-I-d, -t, -a I (ransack, swim) Achievement: \[+d, +t, h-a\] (obliterate, kill) 
Accomplishment: i-I-d, -I-t, -a\] (destroy, 8rrlve) 
259 
Matrix Adjunct Selected 
Features Perspective Type Perspective Word 
\[4-d,-t,4-a pelf \[+d,+t,4- a/ simp, perf When 
\[4-d,-t,:l: a 1 perfeetive l+d,+t,-I-a I strop, perf Cuando 
\[4-d,-t-t,4- ~ perf \[+d,+t,+a\] romp, perf AI 
Figure 3: Selection Charts for When, Cuando, and Al 
showing how selection charts and coercion functions are 
used as a means of parameterization for these processes. 
Selection of Temporal Connectives: Selection 
Charts. In order to ensure that the framework pre- 
sented here is cross-linguistically applicable, we must 
provide a mechanism for handling temporal connective 
selection in languages other than English. For the pur- 
poses of this discussion, we will examine distinctions be- 
tween English and Spanish only. 
Consider the following example: 
(7) (i) John went to the store when Mary arrived. 
(it) John had gone to the store when Mary arrived. 
In Dorr (1991), we discussed the selection of the lexical 
connective when on the basis of the temporal relation 
between the main or matrix clause and the subordinate 
or adjunct clause. 6 For the purposes of this paper, we 
will ignore the temporal component of word selection 
and will focus instead on how the process of word selec- 
tion may be parameterized using the aspectual features 
described in the last section. 
To translate (7)0) and (it) into Spanish, we must 
choose between the lexical tokens cuando and al in or- der to generate the equivalent temporal connective for 
the word when. In the case of (7)(i), there are two pos- 
sible translations, one that uses the connective cuando, 
and one that uses the connective ai: 
(S) (i) Juan fue a la tienda euando Maria lleg6. 
(it) Juan fue a la tienda al llegar Maria. 
Either one of these sentences is an acceptable translation 
for (7)0). However, the same is not true of (7)(it): 7 
(9) (i) Juan habfa ido a la tienda euando Maria lleg6. 
(it) Juan habia ido a la tienda al Ilegar Maria. 
Sentence (9)(i) is an acceptable translation of (7)(it), 
but (9)(it) does not mean the same thing as (7)(it). This 
second sentence implies that John has already gone to 
the store and come back, which is not the preferred read- 
ing. 
In order to establish an association between these con- 
nectives and the aspectual interpretation for the two 
events (i.e., the matrix and adjunct clause), we com- 
pile a table, called a selection chart, for each language 
that specifies the contexts in which each connective may 
be used. Figure 3 shows the charts for when, cuando, 
and al. s 
The selection charts can be viewed as inverted dic- 
tionary entries in that they map features to words, not 
SThis work was based on theories of tense/time by Horn- 
stein (1990) and Allen (1983, 1984). 
rI am indebted to Jorge Lobo (personal communication, 
1991) for pointing this out to me. 
aThe perfective and simple aspects are denoted as per\] 
and strop, respectively. 
words to features. 9 The charts serve as a means of pa- 
rameterization for the program that generates sentences 
from the interlingual representation in that they are al- 
lowed to vary from language to language while the pro- 
cedure for choosing temporal connectives applies cross- 
linguistically, l° The key point to note is that the chart 
for the Spanish connective al is similar to that for the 
English connective when except that the word al requires 
the matrix event to have the +telic feature (i.e., the ma- 
trix action must reach a culmination). This accounts for 
the distinction between cuando and al in sentences (9)(i) 
and (9)(it) above. 11,1~ 
These tables are used for the selection of temporal 
connectives during the generation process (for which the 
relevant index into the tables would be the aspectual 
features associated with the interlingual representation). 
The selection of a temporal connective, then, is simply a 
table look-up procedure based on the aspectual features 
associated with the events. 
Selection and Aspectual Realization of Verbs: 
Coercion Functions. Above, we considered the se- 
lection of temporal connectives without regard to the 
selection and aspectual realization of the lexical items 
that were being connected. Again, to ensure that the 
framework presented here is cross-linguistically applica- 
ble, we must provide a mechanism for handling lexical se- 
lection and aspectual realization in languages other than 
English. 
Consider the English sentence I stabbed Mary. This 
may be realized in at least two ways in Spanish: 13 
(10) (i) Juan le dio pufialadaa a Maria 
(it) Juan le dio una pufialada a Maria 
9 Note, however, that the features correspond to the events 
connected by the words, not to the words themselves. 
1°Because we are not discussing the realization of temporal 
information (i.e., the time relations between the matrix and 
adjunct events), an abbreviated form of the actual chart is 
being used. Specifically, the chart shown in figure 3 assumes 
that the matrix event occurs before the adjunct event. See 
Dorr (1991) and Dorr and Gaasterland (submitted) for more 
details about the relationship between temporal information 
and aspectual information and the actual procedures that are 
used for the selection of temporal connectives. 
11 It has recently been pointed out by Michael Herweg (per- 
sonal communication, 1991b) that the telic feature is not 
traditionally used to indicate a revoked consequence state 
(e.g., the consequence state that results after returning from 
the "going to the store" event), but it is generally intended 
to indicate an irrevocable, culminative, consequence state. 
Thus, it has been suggested that al acts more as a com- 
plementizer than as a "pure" adverbial connective such as 
cuando; this would explain the realization of the adjunct not 
as a tensed adverbial clause, but as an infinitival subordinate 
clause. This possibility is currently under investigation. 
12Space limitations do not permit the enumeration of the 
other selection charts for temporal connectives, but see Dorr 
and Gaasterland (submitted) for additional examples. Some 
of the connectives that have been compiled into tables are: 
after, as soon as, at the moment that, before, between, during, 
since, so long as, until, while, etc. 
13Many other possibilities are available that are not listed 
here (e.g., Juan le acuchill6 a Maria). 
260 
Both of these sentences translate literally to "John gave 
stab wound(s) to Mary." However, the first sentence 
is the repetitive version of the action (i.e., there were 
multiple stab wounds), whereas the second sentence is 
the non-repetitive version of the action (i.e., there was 
only one stab wound). This distinction is character- 
ized by means of the atomicity feature. In (10)(i), the 
event is associated with the features \[+d,+t,-a\], whereas, 
in (10)(it) the event is associated with the features 
\[+d,+t,+a\]. 
According to Bennett et al. (1990), predicates are al- 
lowed to undergo an atomicity "coercion" in which an 
inherently non-atomic predicate (such as dio) may be- 
come atomic under certain conditions. These conditions 
are language-specific in nature, i.e., they depend on the 
lexical-semantic structure of the predicate in question. 
Given the current featural scheme that is imposed on 
top of the lexical-semantic framework, it is easy to spec- 
ify coercion functions for each language. 
We have devised a set of coercion functions for Spanish 
analogous to those proposed for English by Bennett et al. 
The feature coercion parameters for Spanish differ from 
those for English. For example, the atomicity function 
does not have the same applicability in Spanish as it 
does for English. We saw this earlier in sentence (10), in 
which a singular NP verbal object maps a \[-a\] predicate 
into a \[+a\] predicate, i.e., a non-atomic event becomes 
atomic if it is associated with a singular NP object. The 
parameterized mappings that we have constructed for 
Spanish are shown in figure 4(a). For the purposes of 
comparison, the analogous English functions proposed 
by Bennett et al. (1990) are shown in figure 4(b). 14 
Using the functions, we are able to apply the notion 
of feature-based coercion cross-linguistically, while still 
accounting for parametric distinctions. Thus, feature 
coercion provides a useful foundation for a model of in- 
terlingual machine translation. 
A key point about the aspectual features and coercion 
functions is that they allow for a two-way communica- 
tion channel between the two processes of lexical selec- 
tion and aspectual realization, is To clarify this point, we 
return to our example that compares the three English 
verbs, ransack, destroy, and obliterate (see example (4) 
above). Recall that the primary distinguishing feature 
among these three verbs was the notion of telicity (i.e., 
culminated vs. nonculminated). The lexical-semantic 
representation for all three verbs is identical, but the 
telicity feature differs in each case. The verb ransack is 
+telic, obliterate is -telic, and destroy is inherently -telic, 
although it may be coerced to +telic through the use of 
a durative adverbial phrase. Because destroy is a "co- 
14Figure 4(b) contains a subset of the English functions. 
The reader is referred to Bennett et al. (1990) for additional 
functions. The abbreviations C and AC stand for culminator, 
and anti-culminator, respectively. 
lSBecause the focus of this paper is on the lexical-semantic 
representation and associated aspectual parameters, the de- 
tails of the algorithms behind the implementation of the two- 
way communication channel are not presented here; these are 
presented in Dorr and Gaasterland (submitted). We will il- 
lustrate the intuition here by means of example. 
(a) 
(b) 
Mapping 
Telicity (C) 
f(-t)-.+t 
Telicity (AC) 
f(+t)-*-t 
Atomicity 
f(+a)--.*-a 
Parameters 
singular NP 
complements 
' preterit past 
progressive 
morpheme 
imperfect past 
progressive 
morpheme 
plural NP 
complements 
Spanish 
Examples 
Juan le dio una pufialada 
a Marts 
'John stabbed Mary (once)' 
Juan conoci6 a Marts 
'John met Mary (once)' 
Lee estaba pintando un 
cuadro 
'Lee was painting a picture 
(~r some time)' 
Lee conocfa a Maria 
'Lee knew Mary 
(for some time)' 
Chris est£ estornudan¢lo 
'Chris is sneezing 
(repeatedly)' 
Juan le dio pufialadas 
a Maria 
'John stabbed Mary 
(repeatedly)' 
Mapping 
Telicity (C) 
f(-t)--*+t 
Telicity (AC) 
f(+t)-*-t 
Atomicity 
f(+a)--*-a 
Enl$1ish 
Parameters 
singular NP 
complements 
eulminative 
duratives 
progressive 
morpheme 
non-culminative 
duratives 
progressive 
morpheme 
frequency 
adverbials 
Examples 
John ran a mile 
John ran until 6pro 
Lee was painting a picture 
Lee painted the pict'ure 
for an hour 
Chris is sneezing 
Chris ate a sandwich 
everyday 
Figure 4: Parameterization of Coercion Functions for 
English and Spanish 
ercible" verb, it is stored in the lexicon as +telic with a 
flag that forces -telic to be the inherent (i. e., default) set- 
ting. Thus, if we are generating a surface sentence from 
an interlingual form that matches these three verbs but 
we know the value of the telic feature from the context 
of the source-language sentence (i.e., we are able to de- 
termine whether the activity reached a definite point of 
completion), then we will choose ransack, if the setting 
is +telic, or obliterate or destroy, if the setting is -telic. 
In this latter case, only the word destroy will be selected 
if the interlingua includes a component that will be re- 
alized as a durative adverbial phrase. 
Once the aspectual features have guided the lexical 
selection of the verbs, we are able to use these selections 
to guide the aspectual realizations that will be used in 
the surface form. For example, if we have chosen the 
word obliterate we would want to realize the verb in 
the simple past or present (e.g., obliterated or obliter- 
ate) rather than in the progressive (e.g., was obliterating 
or is obliterating). Thus, the aspectual features (and co- 
ercion functions) are used to choose lexical items, and 
the choice of lexical items is used to realize aspectual 
features. 
The coercion functions are crucial for this two-way 
channel to operate properly. In particular, we must take 
care not to blindly forbid non-atomic verbs from being 
realized in the progressive since point activities, which 
are atomic (e.g., tap), are frequently realized in the pro- 
gressive (e.g., he was tapping the table). In such cases 
the progressive morpheme is being used as an iterator 
of several identical atomic events as defined in the func- 
tions shown in figure 4. Thus, we allow "coercible" verbs 
261 
(i.e., those that have a +<feature> specification) to be 
selected and realized with the non-inherent feature set- 
ting if coercion is necessary for the aspectual realization 
of the verb. 
ACQUISITION OF NOVEL LEXICAL 
ENTRIES: DISCOVERING THE LINK 
BETWEEN LCS AND ASPECT 
In evaluating the parameterization framework pro- 
posed here, we will focus on one evaluation metric, 
namely the ease with which lexical entries may be au- 
tomatically acquired from on-line resources. While test- 
ing the framework against this metric, a number of re- 
suits have been obtained, including the discovery of a 
fundamental relationship between aspectual information 
and lexical-semantic information that provides a link be- 
tween the primitives of Jackendoff's LCS representations 
and the features of the aspectual scheme described here. 
Approach. A program has been developed for the au- 
tomatic acquisition of novel lexical entries for machine 
translation. 16 We are in the process of building an En- 
glish dictionary, and intend to use the same approach 
for building dictionaries in other languages, (e.g., Span- 
ish, German, Korean, and Arabic). The program au- 
tomatically acquires aspeetual representations from cor- 
pora (currently the Lancaster/Oslo-Bergen 17 (LOB) cor- 
pus) by examining the context in which all verbs occur 
and then dividing them into four groups: state, activity, 
accomplishment, and achievement. As we noted earlier, 
these four groups correspond to different combinations of 
aspectual features (i.e., telic, atomic, and dynamic) that 
have been imposed on top of the lexieal-semantic frame- 
work. Thus, if we are able to isolate these components 
of verb meaning, we will have made significant progress 
toward our ultimate goal of automatically acquiring full 
lexical-semantic representations of verb meaning. 
The division of verbs into these four groups is based on 
several syntactic tests that are well-defined in the linguis- 
tic literature such as those by Dowty (1979) shown in fig- 
ure 5. is Some tests of verb aspect shown here could not 
be implemented in the acquisition program because they 
require human interpretations. These tests are marked 
by asterisks (*). For example, Test 2 requires human 
interpretation to determine whether or not a verb has 
habitual interpretation in simple present tense. 
The algorithm for determining the aspectual category 
of verbs is shown in figure 6. Note that step 3 applies 
Dowty's tests to a set of sentences corresponding to a 
particular verb until a unique category has been iden- 
tified. In order for this step to succeed, we must en- 
sure that Dowty's tests allow the four categories to be 
uniquely identified. However, a complication arises for 
the state category: out of the six tests that have been 
implemented from Dowty's table, only Test 1 uniquely 
16The implementation details of this program are reported 
in Dorr and Lee (1992). 
lrICAME -- Norwegian Computing Center for the Human- 
ities (tagged version). 
lSThis table is presented in Bennett et al. (1990), p. 250, 
based on Dowry (1979). 
Test STA ACT ACC ACH 
1. X-ing Is grammatical no yes yes yes 
* 2. has habitual interpretation no yes yes yes 
in simple present tense 
3. spend an hour X-ing, yes yes yes no 
X for an hour 
4. take an hour X-ing, no no yes yes 
X in an hour 
* 5. X for an hour entails yes yes no no 
X at all times in the hour 
* 6. Y is X-ing entails no yes no no 
Y has X-ed 
7. complement of stop yes yes yes no 
8. complement of finish no no yes no 
* 9. ambiguity with almost no no yes no 
*10. Y X-ed in an hour entails no no yes no 
Y was X-ing during 
that hour 
11. occurs with no yes yes no 
studiously, carefully, etc. 
Figure 5: Dowty's Eleven Tests of Verb Aspect 
1. Pick out main verbs from all sentences in the corpus and store 
them in a list called VERBS. 
2. For each verb v in VERBS, find all sentences containing v and 
store them in an array SENTENCES\[i\] (where i is the indexical 
position of v in VERBS). 
3. For each sentence set Sj in SENTENCE\[j\], loop through each 
sentence s in Sj: 
(a) Loop through each test t in figure 5. 
(b) See if t applies to s; if so, eliminate all aspectual categories 
with a NO in the row of figure 5 corresponding to test t. 
(c) Eliminate possibilities until a unique aspectual category is 
identified or until all sentences in SENTENCES have been 
exhausted. 
Figure 6: Algorithm for Determining Aspectual Cate- 
gories 
sets states apart from the other three aspectual cate- 
gories. That is, Test 1 is the only implemented test that 
has a value in the first column that is different from the 
other three columns. Note, however, that the value in 
this column is NO, which poses a problem for the above 
algorithm. Herein lies one of the major stumbling blocks 
for the extraction of information from corpora: it is only 
possible to derive new information in cases where there 
is a YES value in a given column. By definition, a cor- 
pus only provides positive evidence; it does not provide 
negative evidence. We cannot say anything about sen- 
tences that do not appear in the corpus. Just because 
a given sentence does not occur in a particular sample 
of English text does not mean that it can never show 
up in English. This means we are relying solely on the 
information that does appear in the corpus, i.e., we are 
only able to learn something new about a verb when it 
corresponds to a YES in one of the rows of figure 5.19 
Given that the identification of stative verbs could not 
be achieved by Dowty's tests alone, a number of hypothe- 
ses were made in order to identify states by other means. 
A preliminary analysis of the sentences in the corpus re- 
veals that progressive verbs are generally preceded by 
verbs such as be, like, hate, go, stop, start, etc. These 
19 Note that this is consistent with principles of recent mod- 
els of language acquisition. For example, the Subset Principle 
proposed by Berwick (1985, p. 37) states that "the learner 
should hypothesize languages in such a way that positive ev- 
idence can refute an incorrect guess." 
262 
Verbs Jackendoff 
Primitive 
be BE 
like BE 
hate BE 
go GO 
stop GO 
start GO 
finish GO 
avoid STAY 
continue STAY 
keep STAY 
Aspectual 
Category 
state ~STA) 
state (STA) 
state (STA) 
non-state q ACH) 
non-state ~ ACH) 
non-state q ACH) 
non-state q ACH) 
non-state ACT) 
non-state ACT) 
non-state ACT) 
Aspectual 
Features 
\[-d l +d, +t, +a\] 
+d, +t, +a l +d, +t, +a\] 
+d, +t, -t-a\] 
l+d, -t l 
\[+d, -t\] 
\[+d, -t\] 
Figure 7: Circumstantial Verbs Categorized By Jackend- 
off's Primitives 
Test to see if X appears in the progressive. 
1. If YES, then apply one of the tests that distinguishes ac- 
tivities from achievements (i.e., Test 3, Test 4, or Test 7). 
2. If NO, apply Test 3 to rule out achievement or Test 4 to 
uniquely identify as an achievement. 
3. Finally, if the aspectual category is not yet uniquely iden- 
tified, either apply Test 11 to rule out activity or assume 
state. 
Figure 8: Algorithm for Identifying Stative Verbs 
verbs fall under a lexical-semantic category identified by 
Jackendoff (1983, 1990) as the circumstantial category. 
Based on this observation, the following hypothesis has 
been made: 
Hypothesis 1: The only types of verbs that are allowed to 
precede progressive verbs are circumstantial verbs. 
Circumstantial verbs subsume stative verbs, but they 
also include verbs in other categories. In terms of 
the lexical-semantic primitives proposed by Jackendoff 
(1983, 1990), the circumstantial verbs found in a sub- 
set of the corpus are categorized as shown in figure 7. 
An intriguing result of this categorization is that the 
circumstantial verbs provide a systematic partitioning 
of Dowty's aspectual categories (i.e., states, activities, 
and achievements) into primitives of Jackendoff's system 
(i.e., BE, STAY, and GO). Thus, the analysis of the cor- 
pora has provided a crucial link between the primitives of 
Jackendoff's LCS representation and the features of the 
aspectual scheme described earlier. If this is the case, 
then the framework has proven to be well-suited to the 
task of automatic construction of conceptual structures 
from corpora. 
Assuming this partitioning is correct and complete, 
Hypothesis 1 can be refined as follows: 
Hypothesis 1'~ The only types of verbs that are allowed to 
precede progressive verbs are states, achievements, and activi- 
ties. 
If this hypothesis is valid, the program is in a better posi- 
tion to identify stative verbs because it corresponds to a 
test that requires positive evidence rather than negative 
evidence. The hypothesis can be described by adding 
the following line to figure 5: 
Verbs Aspectual Category(s) 
doing (ACC) 
facing (ACC ACT) 
asking (ACC ACT) 
made (ACC) 
drove ~ACC ACT) 
welcome (STA ACC ACT ACH) 
emphasized (STA ACC ACT ACH) 
thanked (ACC ACT STA) 
staged (ACC) 
make (ACC) 
continue ~ACC ACT) 
writes ~ACC) 
building ~ACC) 
running (ACC ACT) 
paint { ACC) 
finds ( ACC ACT) 
arrives { ACC ACT) 
jailed {ACC ACT STA) 
nominating (ACH ACT ACC 
read ( ACC ACT) ) 
ensure (STA ACC ACT ACH) 
act ( ACT ACC) 
carry (ACC) 
exercise (ACC) 
impose (STA ACC ACT ACH) 
contain ~STA ACC ACT ACH) 
infuriate (ACC ACT) 
Figure 9: Aspectual Classification Results 
whether X is stative. 2° 
Another hypothesis that has been adopted pertains to 
the distribution of progressives with respect to the verb 
go: 
Hypothesis ~z The only types of progressive verbs that are 
allowed to follow the verb go are activities. 
This hypothesis was adopted after it was discovered 
that constructions such as go running, go skiing, go 
swimming, etc. appeared in the corpus, but not construc- 
tions such as go eating, go writing, etc. The hypothesis 
can be described by adding the following line to figure 5: 
\[ Test \[ STA \[ ACT \[ ACC \] ACH \[ 
13. go X-ing is grammatical no yes no no 
The combination of Dowty's tests and these hypoth- 
esized tests allows the four aspectual categories to be 
more specifically identified. 
Results and Future Work. Preliminary results have 
been obtained from running the program on 219 sen- 
tences of the LOB corpus (see figure 9). 21 Note that the 
program was not able to pare down the aspectual cate- 
gory to one in every case. We expect to have a significant 
improvement in the classification results once the sample 
size is increased. 
Presumably more tests would be needed for additional 
improvements in results. For example, we have not pro- 
posed any tests that would guarantee the unique identi- 
fication of accomplishments. Such tests are the subject 
of future research. 
I Te., i I I Ace i AC. I 12. X <verb>-in~ is ~rammatical yes yes no yes 
Because there is a YES in the column headed by STA, 
verbs satisfying this test are potentially stative. Thus, 
once a verb X is found that satisfies this test, we apply 
the (heuristic) algorithm shown in figure 8 to determine 
2°Note that this algorithm does not guarantee that states 
will be correctly identified in all cases given that step 3 is a 
heuristic assumption. However, if Test 12 has applied, and 
state is still an active possibility, it is considerably safer to 
assume the verb is a state than it would be otherwise because 
we have eliminated accomplishments. 
21 For brevity, only a subset of the verbs are shown here. 
263 
In addition, research is currently underway to deter- 
mine the restrictions (analogous to those shown in fig- 
ure 5) that exist for other languages (e.g., Spanish, Ger- 
man, Korean, and Arabic). Because the program is para- 
metrically designed, it is expected to operate uniformly 
on corpora in other languages as well. 
Another future area of research is the automatic ac- 
quisition of parameter settings for the construction of 
selection charts and aspectual coercion mappings on a 
per-language basis. 
SUMMARY 
This paper has examined a two-level knowledge repre- 
sentation model for machine translation that integrates 
aspectual information based on theories by Bach (1986), 
Comrie (1976), Dowty (1979), mourelatos (1981), Pas- 
sonneau (1988), Pustejovsky (1988, 1989, 1991), and 
Vendler (1967), and more recently by Bennett et al. 
(1990) and Moens and Steedman (1988), with lexical- 
semantic information based on Jackendoff (1983, 1990). 
We have examined the question of cross-linguistic ap- 
plicability showing that the integration of aspect with 
lexical-semantics is especially critical in machine transla- 
tion when there are a large number of temporal connec- 
tives and verbal selection/realization possibilities that 
may be generated from a lexical semantic representa- 
tion. Furthermore, we have illustrated that the se- 
lection/realization processes may be parameterized, by 
means of selection charts and coercion functions, so that 
the processes may operate uniformly across more than 
one language. Finally, we have discussed the application 
of the theoretical foundations to the automatic acquisi- 
tion of aspectual representations from corpora in order to 
augment the lexical-semantic representations that have 
already been created for a large number of verbs. 

REFERENCES 
Allen, James. F. (1983) "Maintaining Knowledge about Temporal In- 
tervals," Communications ol the ACM 26:11,832-843. 
Allen, James. F. (1984) "Towards a General Theory of Action and 
Time," Artificial Intelligence 23:2, 123-160. 
Bach, Emmon (1986) "The Algebra of Events," Linguistics and Phi- 
losophy 9, 5-16. 
Bennett, Winfield S., Tangs Herlick, Katherine Hoyt, Joseph Liro and 
Ana Santistebem (1990) "A Computational Model of Aspect and 
Verb Semantics," Machine Translation 4:4, 247-280. 
Berwick, Robert C. (1985) The Acquisition of Syntactic Knowledge, 
MIT Press, Cambridge, MA. 
Cowrie, Bernard (1976) Aspect, Cambridge University Press, Cam- 
bridge, England. 
Dorr, Bonnie J. (1987) "UNITRAN: A Principle-Ba~ed Approach to 
Machine Translation," AI Technical Report 1000, Master of Science 
thesis, Department of Electrical Engineering and Computer Science, 
Massachusetts Institute of Technology. 
Dorr, Bonnie J. (1990a) "Solving Thematic Divergences in Machine 
Translation," Proceedings of the ~Sth Annual Conference of the 
Association for Computational Linguistics, University of Pitts- 
burgh, Pittsburgh, PA, 127-134. 
Dorr, Bonnie J. (1990b) "A Cross-Linguistic Approach to Machine 
Translation," Proceedings of the Third International Conference 
on Theoretical and Methodological Issues in Machine Translation 
of Natural Languages, Linguistics Research Center, The University 
of Texas, Austin, TX, 13-32. 
Dorr, Bonnie J. (1991) "A Two-Level Knowledge Representation for 
Machine Translation: Lexical Semantics and Tense/Aspect," Pro- 
ceedings of the Lexical Semantics and Knowledge Representation 
Workshop, ACL-91, University of California, Berkeley, CA, 250- 
263. 
Dorr, Bonnie J. and Ki Lee (1992) "Building a Lexicon for Machine 
Translation: Use of Corpora for Aspectual Classification of Verbs," 
Institute for Advanced Computer Studies, University of Maryland, 
UMIACS TR 92-41, CS TR 2876. 
Dorr, Bonnie J., and Terry Gaasterland (submitted) "Using Temporal 
and Aspectual Knowledge to Generate Event Combinations from 
a Temporal Database," Third International Conference on Prin- 
ciples of Knowledge Representation and Reasoning, Cambridge, 
MA, 1992. 
Dowty, David (1979) Word Meaning and Montague Grammar, Reidel, 
Dordrecht, Netherlands. 
Herweg, Michael (1991a) "Aspectual Requirements of Temporal Con- 
nectives: Evidence for a Two-level Approach to Semantics," Pro- 
ceedings of the Lexical Semantics and Knowledge Representation 
Workshop, ACL-91, University of California, Berkeley, CA, 152- 
164. 
Hornstein, Norbert (1990) As Time Goes By, MIT Press, Cambridge, 
MA. 
ICAME -- Norwegian Computing Center for the Humanities (tagged 
version) Laneaster/Oslo-Bergen Corpus, Bergen University, Nor- 
way. 
Jackendoff, Hay S. (1983) Semantics and Cognition, MIT Press, Cam- 
bridge, MA. 
Jackendoff, Ray S. (1990) Semantic Structures, MIT Press, Cam- 
bridge, MA. 
Lobs, Jorge (1991) personal communication. 
Moens, Marc and Mark Steedman (1988) "Temporal Ontology and 
Temporal Reference," Computational Linguistics 14:2, 15-28. 
Mourelatos, Alexander (1981) "Events, Processes and States," in 
Tense and Aspect, P. J. Tedeschi and A. Zaenen (eds.), Academic 
Press, New York, NY. 
Passonneau, Rebecca J. (1988) "A Computational Model of the Seman- 
tics of Tense and Aspect," Computational Linguistics 14:2, 44-60. 
Pustejovsky, James (1988) "The Geometry of Events," Center for Cog- 
nitive Science, Massachusetts Institute of Technology, Cambridge, 
MA, Lexicon Project Working Papers #24. 
Pustejovsky, James (1989) "The Semantic Representation of Lexicai 
Knowledge," Proceedings of the First Annual Workshop on Lexieal 
Acquisition, IJCAI.89, Detroit, Michigan. 
Pustejovsky, James (1991) "The Syntax of Event Structure," Cogni- 
tion. 
Tenny, Carol (1987) "Grammatiealizing Aspect and Affectedness," 
Ph.D. thesis, Department of Electrical Engineering and Computer 
Science, Massachusetts Institute of Technology. 
Tenny, Carol (1989) "The Aspectual Interface Hypothesis," Center 
for Cognitive Science, Massachusetts Institute of Technology, Cam- 
bridge, MA, Lexicon Project Working Papers #31. 
Vendler, Zeno (1967) "Verbs and Times," Linguistics in Philosophy, 
97-121. 
Yip, Kenneth M. (1985) "Tense, Aspect and the Cognitive Represen- 
tation of Time," Proceedings of the 23rd Annual Conference of the 
Association for Computational Linguistics, Chicago, IL, 18-26. 
