UNDERSTANDING 
NATURAL LANGUAGE INSTRUCTIONS: 
THE CASE OF PURPOSE CLAUSES 
Barbara Di Eugenio * 
Department of Computer and Information Science 
University of Pennsylvania 
Philadelphia, PA 
dieugeni@linc.cis.upenn.edu 
ABSTRACT 
This paper presents an analysis of purpose clauses in 
the context of instruction understanding. Such analysis 
shows that goals affect the interpretation and / or exe- 
cution of actions, lends support to the proposal of using 
generation and enablement to model relations between 
actions, and sheds light on some inference processes 
necessary to interpret purpose clauses. 
INTRODUCTION 
A speake~ (S) gives instructions to a hearer CrI) in 
order to affect H's behavior. Researchers including 
(Winograd, 1972), (Chapman, 1991), (Vere and Bick- 
more, 1990), (Cohen and Levesque, 1990), (Alterman et 
al., 1991) have been and are addressing many complex 
facets of the problem of mapping Natural Language in- 
structions onto an agent's behavior. However, an aspect 
that no one has really considered is computing the ob- 
jects of the intentions H's adopts, namely, the actions to 
be performed. In general, researchers have equated such 
objects with logical forms extracted from the NL input. 
This is perhaps sufficient for simple positive impera- 
tives, but more complex imperatives require that action 
descriptions be computed, not simply extracted, from the 
input instruction. To clarify my point, consider: 
Ex. 1 a) Place a plank between two ladders. 
b) Place a plank between two ladders 
to create a simple scaffold. 
In both a) and b), the action to be executed is place 
a plank between two ladders. However, Ex. 1.a would 
be correctly interpreted by placing the plank anywhere 
between the two ladders: this shows that in b) H must 
be inferring the proper position for the plank from the 
expressed goal to create a simple scaffold. Therefore, 
the goal an action is meant to achieve constrains the 
interpretation and / or the execution of the action itself. 
The infinitival sentence in Ex. 1.b is a purpose clause, 
*Mailing addxess: IRCS - 3401, Walnut St - Suite 40(0 - 
Philadelphia, PA, 19104 - USA. 
which, as its name says, expresses the agent's purpose 
in performing a certain action. The analysis of purpose 
clauses is relevant to the problem of understanding Nat- 
ural Language instructions, because: 
1. Purpose clauses explicitly encode goals and their 
interpretation shows that the goals that H adopts 
guide his/her computation of the action(s) to per- 
form. 
2. Purpose clauses appear to express generation or en- 
ablement, supporting the proposal, made by (Allen, 
1984), (Pollack, 1986), (Grosz and Sidner, 1990), 
(Balkansld, 1990), that these two relations are nec- 
essary m model actions. 
After a general description of purpose clauses, I will 
concentrate on the relations between actions that they 
express, and on the inference processes that their in- 
terpretation requires. I see these inferences as instan- 
tiations of general accommodation processes necessary 
to interpret instructions, where the term accommodation 
is borrowed from (Lewis, 1979). I will conclude by 
describing the algorithm that implements the proposed 
inference processes. 
PURPOSE CLAUSES 
I am not the first one to analyze purpose clauses: how- 
ever, they have received attention almost exclusively 
from a syntactic point of view - see for example (Jones, 
1985), (l-Iegarty, 1990). Notice that I am not using the 
term purpose clause in the technical way it has been 
used in syntax, where it refers to infinitival to clauses 
adjoined to NPs. In contrast, the infinitival clauses I 
have concentrated on are adjoined to a matrix clause, 
and are termed rational clauses in syntax; in fact all the 
data I will discuss in this paper belong to a particular 
subclass of such clauses, subject-gap rational clauses. 
As far as I know, very little attention has been paid 
to purpose clauses in the semantics literature: in (1990), 
Jackendoff briefly analyzes expressions of purpose, goal, 
or rationale, normally encoded as an infinitival, in order 
120 
to-phrase, or for-phrase. He represents them by means 
of a subordinating function FOR, which has the adjunct 
clause as an argument; in turn, FOR plus its argument 
is a restrictive modifier of the main clause. However, 
Jackendoff's semantic decomposition doesn't go beyond 
the construction of the logical form of a sentence, and 
he doesn't pursue the issue of what the relation between 
the actions described in the matrix and adjunct really is. 
The only other work that mentions purpose clauses in 
a computational setting is (Balkanski, 1991). However, 
she doesn't present any linguistic analysis of the data; as 
I will show, such analysis raises many interesting issues, 
such as t: 
• It is fairly clear that S uses purpose clauses to explain 
to H the goal/~ to whose achievement the execution of 
contributes. However, an important point that had been 
overlooked so far is that the goal/~ also constrains the 
interpretation of ~, as I observed with respect to Ex. 1.b. 
Another example in point is: 
Ex. 2 Cut the square in half to create two triangles. 
The action to be performed is cutting the square in half. 
However, such action description is underspecified, in 
that there is an infinite number of ways of cutting a 
square in half: the goal create two triangles restricts 
the choice to cutting the square along one of the two 
diagonals. 
• Purpose clauses relate action descriptions at different 
levels of abstraction, such as a physical action and an 
abstract process, or two physical actions, but at different 
levels of granularity: 
Ex. 3 Heat on stove to simmer. 
• As far as what is described in purpose clauses, I have 
been implying that both matrix and purpose clauses de- 
scribe an action, c~ and/~ respectively. There are rare 
cases - in fact, I found only one - in which one of the 
two clauses describes a state ~r: 
Ex. 4 To be successfully covered, a wood wall must be 
flat and smooth. 
I haven't found any instances in which both matrix and 
purpose clauses describe a state. Intuitively, this makes 
sense because S uses a purpose clause to inform H of 
the purpose of a given action 2 
• In most cases, the goal /~ describes a change in the 
world. However, in some cases 
1. The change is not in the world, but in H's knowl- 
edge. By executing o~, H can change the state of 
his knowledge with respect to a certain proposition 
or to the value of a certain entity. 
1I collected one hundred and one consecutive instances of 
purpose clauses from a how-to-do book on installing wall cov- 
erings, and from two craft magazines. 
~There are clearly other ways of describing that a state is 
the goal of a certain action, for example by means of so~such 
that, but I won't deal with such data here. 
Ex. 5 You may want to hang a coordinating border 
around the room at the top of the walls. To deter- 
mine the amount of border, measure the width (in 
feet) of all walls to be covered and divide by three. 
Since borders are sold by the yard, this will give you 
the number of yards needed. 
Many of such examples involve verbs such as 
check, make sure etc. followed by a that- 
complement describing a state ~b. The use of such 
verbs has the pragmatic effect that not only does H 
check whether ~b holds, but, if ~b doesn't hold, s/he 
will also do something so that ff comes to hold. 
Ex. 6 To attach the wires to the new switch, use the 
paper clip to move the spring type clip aside and 
slip the wire into place. Tug gently on each wire to 
make sure it's secure. 
2. The purpose clause may inform H that the world 
should not change, namely, that a given event 
should be prevented from happening: 
Ex. 7 Tape raw edges of fabric to prevent threads 
from raveling as you work. 
• From a discourse processing point of view, interpret- 
ing a purpose clause may affect the discourse model, in 
particular by introducing new referents. This happens 
when the effect of oL is to create a new object, and/~ 
identifies it. Verbs frequently used in this context are 
create, make, form etc. 
Ex. 8 Join the short ends of the hat band to form a circle. 
Similarly, in Ex. 2 the discourse referents for the tri- 
angles created by cutting the square in half, and in Ex. 5 
the referent for amount of border are introduced. 
RELATIONS BETWEEN ACTIONS 
So far, I have mentioned that oe contributes to achiev- 
ing the goal/~. The notion of contribution can be made 
more specific by examining naturally occurring purpose 
clauses. In the majority of cases, they express genera- 
tion, and in the rest enablement. Also (Grosz and Sid- 
ner, 1990) use contribute as a relation between actions, 
and they define it as a place holder for any relation ... 
that can hold between actions when one can be said to 
contribute (for example, by generating or enabling) to 
the performance of the other. However, they don't jus- 
tify this in terms of naturally occurring data. Balkanski 
(1991) does mention that purpose clauses express gen- 
eration or enablement, but she doesn't provide evidence 
to support this claim. 
GENERATION 
Generation is a relation between actions that has been 
extensively studied, first in philosophy (Goldman, 1970) 
and then in discourse analysis (Allen, 1984), (Pollack, 
1986), (Grosz and Sidner, 1990), (Balkanski, 1990). 
According to Goldman, intuitively generation is the re- 
lation between actions conveyed by the preposition by 
in English - turning on the light by flipping the switch. 
121 
More formally, we can say that an action a conditionally 
generates another action/~ iff 3: 
1. a and/~ are simultaneous; 
2. a is not part of doing/~ (as in the case of playing 
a C note as part of playing a C triad on a piano); 
3. when a occurs, a set of conditions C hold, such that 
the joint occurrence of a and C imply the occur- 
rence of/L In the case of the generation relation 
between flipping the switch and turning on the light, 
C will include that the wire, the switch and the bulb 
are working. 
Although generation doesn't hold between o~ and fl if 
is part of a sequence of actions ,4 to do/~, generation 
may hold between the whole sequence ,4 and/~. 
Generation is a pervasive relation between action de- 
scriptions in naturally occurring data. However, it ap- 
pears from my corpus that by clauses are used less fre- 
quently than purpose clauses to express generation 4: 
about 95% of my 101 purpose clauses express gener- 
ation, while in the same corpus there are only 27 by 
clauses. It does look like generation in instructional text 
is mainly expressed by means of purpose clauses. They 
may express either a direct generation relation between 
and/~, or an indirect generation relation between 
and/~, where by indirect generation I mean that ~ be- 
longs to a sequence of actions ,4 which generates 8. 
ENABLEMENT 
Following first Pollack (1986) and then Balkanski 
(1990), enablement holds between two actions ~ and 
/~ if and only if an occurrence of ot brings about a set of 
conditions that are necessary (but not necessarily suffi- 
cien 0 for the subsequent performance of 8. 
Only about 5% of my examples express enablement: 
Ex. 9 Unscrew the protective plate to expose the box. 
Unscrew the protective plate enables taking the plate off 
which generates exposing the box. 
GENERATION AND ENABLEMENT IN 
MODELING ACTIONS 
That purpose clauses do express generation and enable- 
ment is a welcome finding: these two relations have 
been proposed as necessary to model actions (Allen, 
1984), (Pollack, 1986), (Grosz and Sidner, 1990), 
(Balkanski, 1990), but this proposal has not been jus- 
tiffed by offering an extensive analysis of whether and 
how these relations are expressed in NL. 
3Goldman distinguishes among four kinds of generation re- 
lations: subsequent work has been mainly influenced by con- 
ditional generation. 
4Generation can also be expressed with a simple free ad- 
junct; however, this use of free adjuncts is not very common 
- see 0hrebber and Di Eugenio, 1990). 
122 
A further motivation for using generation and enable- 
ment in modeling actions is that they allow us to draw 
conclusions about action execution as well - a particu- 
larly useful consequence given that my work is taking 
place in the framework of the Animation from Natural 
Language - AnimNL project (Badler eta/., 1990; Web- 
ber et al., 1991) in which the input instructions do have 
to be executed, namely, animated. 
As has already been observed by other researchers, ff 
generates /~, two actions are described, but only a, 
the generator, needs to be performed. In Ex. 2, there is 
no creating action per se that has to be executed: the 
physical action to be performed is cutting, constrained 
by the goal as explained above. 
In contrast to generation, if a enables/~, after execut- 
ing or, fl still needs to be executed: a has to temporally 
precede/~, in the sense that a has to begin, but not nec- 
essarily end, before/3. In Ex. 10, ho/d has to continue 
for the whole duration offal/: 
Ex. 10 Hold the cup under the spigot to fill it with coffee. 
Notice that, in the same way that the generatee affects 
the execution of the generator, so the enabled action 
affects the execution of the enabling action. Consider 
the difference in the interpretation of to in go to the 
mirror, depending upon whether the action to be enabled 
is seeing oneself or carrying the mirror somewhere else. 
INFERENCE PROCESSES 
So far, I have been talking about the purpose clause 
constraining the interpretation of the matrix clause. I 
will now provide some details on how such constraints 
are computed. The inferences that I have identified so 
far as necessary to interpret purpose clauses can be de- 
scribed as 
1. Computing a more specific action description. 
2. Computing assumptions that have to hold for a cer- 
tain relation between actions to hold. 
Computing more specific action descriptions. 
In Ex. 2 - Cut the square in half to create two triangles 
- it is necessary to find a more specific action al which 
will achieve the goal specified by the purpose clause, as 
shown in Fig. 1. 
For Ex. 2 we have fl = create two triangles, o~ = 
cut the square in half, ~1 = cut the square in half along 
the diagonal. The reader will notice that the inputs to 
accommodation are linguistic expressions, while its out- 
puts are predicate - argument structures: I have used 
the latter in Fig. 1 to indicate that accommodation infers 
relations between action types. However, as I will show 
later, the representation I adopt is not based on predi- 
cate - argument structures. Also notice that I am using 
Greek symbols for both linguistic expressions and action 
types: the context should be sufficient to disambiguate 
which one is meant. 
Computing assumptions. Let's consider: 
(create two 
(cut the 
triangles) 
square in hal0 
> accommodation 
(create (agent, two-triangles)) 
/~ (cut ~g~~ilt (2g21t' sZi'al~ng~~igonal))) 
Figure 1: Schematic depiction of the first kind of accommodation 
accommodation 
A A... A .... l 2 1 
¢g 
Figure 2: Schematic depiction of the second kind of accommodation 
Ex. 11 Go into the other room to get the urn of coffee. 
Presumably, H doesn't have a particular plan that deals 
with getting an urn of coffee. S/he will have a generic 
plan about get x, which s/he will adapt to the instructions 
S gives him 5. In particular, H has to find the connection 
between go into the other room and get the urn of coffee. 
This connection requires reasoning about the effects of 
go with respect to the plan get x; notice that the (most 
direc0 connection between these two actions requires 
the assumption that the referent of the urn of coffee is 
in the other room. Schematically, one could represent 
this kind of inference as in Fig. 2 -/~ is the goal, ~ the 
instruction to accommodate, Ak the actions belonging 
to the plan to achieve t, C the necessary assumptions. 
It could happen that these two kinds of inference need 
to be combined: however, no example I have found so 
far requires it. 
INTERPRETING Do a to do I~ 
In this section, I will describe the algorithm that im- 
5Actually H may have more than one single plan for get x,. 
in which case go into the other room may in fact help to select 
the plan the instructor has in mind. 
123 
plements the two kinds of accommodation described in 
the previous section. Before doing that, I will make 
some remarks on the action representation I adopt and 
on the structure of the intentions - the plan graph - that 
my algorithm contributes to building. 
Action representation. To represent action types, I use 
an hybrid system (Brachman et al., 1983), whose primi- 
tives are taken from Jackendoff's Conceptual Structures 
(1990); relations between action types are represented in 
another module of the system, the action library. 
I'd like to spend a few words justifying the choice 
of an hybrid system: this choice is neither casual, nor 
determined by the characteristics of the AnimNL project. 
Generally, in systems that deal with NL instructions, 
action types are represented as predicate - argument 
structures; the crucial assumption is then made that the 
logical form of an input instruction will exactly match 
one of these definitions. However, there is an infinite 
number of NL descriptions that correspond to a basic 
predicate - argument structure: just think of all the pos- 
sible modifiers that can be added to a basic sentence 
containing only a verb and its arguments. Therefore it 
is necessary to have a flexible knowledge representation 
system that can help us understand the relation between 
the input description and the stored one. I claim that 
hybrid KR systems provide such flexibility, given their 
virtual lattice structure and the classification algorithm 
operating on the lattice: in the last section of this paper 
I will provide an example supporting my claim. 
Space doesn't allow me to deal with the reason why 
Conceptual Structures are relevant, namely, that they are 
useful to compute assumptions. For further details, the 
interested reader is referred to (Di Eugenio, 1992; Di 
Eugenic) and White, 1992). 
Just a reminder to the reader that hybrid systems have 
two components: the terminological box, or T-Box, 
where concepts are defined, and on which the classi- 
fication algorithm works by computing subsumption re- 
lations between different concepts. The algorithm is cru- 
cial for adding new concepts to the KB: it computes the 
subsumption relations between the new concept and all 
the other concepts in the lattice, so that it can "Position" 
the new concept in the right place in the lattice. The 
other component of an hybrid system is the assertional 
box, or A-box, where assertions are stored, and which 
is equipped with a theorem-prover. 
In my case, the T-Box contains knowledge about ac- 
tion types, while assertions about individual actions - 
instances of the types - are contained in the A-Box: 
such individuals correspond to the action descriptions 
contained in the input instructions 6 
The action library contains simple plans relating ac- 
tions; simple plans are either generation or enablement 
relations between pairs: the first member of the pair is 
either a single action or a sequence of action, and the 
second member is an action. In case the first member of 
the pair is an individual action, I will talk about direct 
generation or enablement. For the moment, generation 
and enablement are represented in a way very similar to 
(Balkanski, 1990). 
The plan graph represents the structure of the inten- 
tions derived from the input instructions. It is composed 
of nodes that contain descriptions of actions, and arcs 
that denote relations between them. A node contains 
the Conceptual Structures representation of an action, 
augmented with the consequent state achieved after the 
execution of that action. The arcs represent, among oth- 
ers: temporal relations; generation; enablement. 
The plan graph is built by an interpretation algorithm 
that works by keeping track of active nodes, which for 
the moment include the goal currently in focus and the 
nodes just added to the graph; it is manipulated by var- 
ious inference processes, such as plan expansion, and 
plan recognition. 
My algorithm is described in Fig. 3 7. Clearly the 
inferences I describe are possible only because I rely 
~Notice that these individuals are simply instances of 
generic concepts, and not necessarily action tokens, namely, 
nothing is asserted with regard to their happening in the world. 
rAs I mentioned earlier in the paper, the Greek symbols 
on the other AnimNL modules for 1) parsing the in- 
put and providing a logical form expressed in terms of 
Conceptual Structures primitives; 2) managing the dis- 
course model, solving anaphora, performing temporal 
inferences etc (Webber eta/., 1991). 
AN EXAMPLE OF THE ALGORITHM 
I will conclude by showing how step 4a in Fig. 3 takes 
advantage of the classification algorithm with which hy- 
brid systems are equipped. 
Consider the T-Box, or better said, the portion of T- 
Box shown in Fig. 4 s. 
Given Ex. 2 - Cut the square in half to create two 
triangles - as input, the individual action description 
cut (the) square in half will be asserted in the A-Box 
and recognized as an instance of ~ - the shaded concept 
cut (a) square in half - which is a descendant of cut 
and an abstraction of o: - cut (a) square in half along 
the diagonal, as shown in Fig. 5 9. Notice that this 
does not imply that the concept cut (a) square in half 
is known beforehand: the classification process is able 
to recognize it as a virtual concept and to find the right 
place for it in the lattice 10. Given that a is ancestor 
of o J, and that oJ generates/~ - create two triangles, the 
fact that the action to be performed is actually o~ and not 
oL can be inferred. This implements step 4(a)ii. 
The classification process can also help to deal with 
cases in which ~ is in conflict with to - step 4(a)iv. If 
were cut (a) square along a perpendicular axis, a con- 
flict with o~ - cut (a) square in half along the diagonal 
- would be recognized. Given the T-Box in fig. 4, the 
classification process would result in o~ being a sister to 
w: my algorithm would try to unify them, but this would 
not be possible, because the role fillers of location on 
and w cannot be unified, being along(perpendicular- 
axis) and along(diagonal) respectively. I haven't ad- 
dressed the issue yet of which strategies to adopt in case 
such a conflict is detected. 
Another point left for future work is what to do when 
step 2 yields more than one simple plan. 
The knowledge representation system I am using is 
BACK (Peltason et al., 1989); the algorithm is being 
implemented in QUINTUS PROLOG. 
refer both to input descriptions and to action types. 
SThe reader may find that the representation in Fig. 4 is 
not very perspicuous, as it mixes linguistic expressions, such 
as along(diagonal), with conceptual knowledge about entities. 
Actually, roles and concepts are expressed in terms of Con- 
ceptual Structures primitives, which provide a uniform way 
of representing knowledge apparently belonging to different 
types. However, a T-Box expressed in terms of Conceptual 
Structures becomes very complex, so in Fig. 4 I adopted a 
more readable representation. 
9The agent role does not appear on cut square in half in 
the A-Box for the sake of readability. 
1°In fact, such concept is not really added to the lattice. 
124 
Input: the Conceptual Structures logical forms for ~ and t, the current plan graph, and the list of active nodes. 
1. Add to A-Box individuals corresponding to the two logical forms. Set flag ACCOM if they don't exactly match 
known concepts. 
2. Retrieve from the action library the simple plan(s) associated with /5 - generation relations in which /5 is the 
generate., enablement relations in which/5 is the enablee. 
3. If ACCOM is not set 
(a) If there is a direct generation or enablement relation between ~ and/5, augment plan graph with the structure 
derived from it, after calling compute-assumptions. 
(b) If there is no such direct relation, recursively look for possible connections between e and the components 7i 
of sequences that either generate or enable/5. 
Augment plan graph, after calling c omput e- a s s umpt i on s. 
4. If ACCOM is set, 
(a) If there is ~a such that oJ directly generates or enables/5, check whether 
i. w is an ancestor of c~: take c~ as the intended action. 
ii. ~o is a descendant of c~: take o~ as the intended action. 
iii. If w and e are not ancestors of each other, but they can be unified - all the information they provide 
is compatible, as in the case of cut square in half along diagonal and cut square carefully - then their 
unification w U c~ is the action to be executed. 
iv. If o: and ~ are not ancestors of each other, and provide conflicting information - such as cut square along 
diagonal and cut square along perpendicular axis - then signal failure. 
(b) If there is no such w, look for possible connections between ~ and the components 7i of sequences that either 
generate or enable/5, as in step 3b. Given that ~ is not known to the system, apply the inferences described 
in 4a to c~ and 7/. 
Figure 3: The algorithm for Do ~ to do 
125 
O earnest @ role 
V/R (Value Rcm~iction) /  ,.on .... 
Figure 4: A portion of the action hierarchy 
individual 
,,,.,,.,,,,,..,, instantiates 
T_.OX -,,i .\ 
~,~ /--~ 
location / / 
A-BOX 
Figure 5: Dealing with less specific action descriptions 
126 
CONCLUSIONS 
I have shown that the analysis of purpose clauses 
lends support to the proposal of using generation and 
enablement to model actions, and that the interpretation 
of purpose clauses originates specific inferences: I have 
illustrated two of them, that can be seen as examples of 
accommodation processes (Lewis, 1979), and that show 
how the bearer's inference processes are directed by the 
goal(s) s/he is adopting. 
Future work includes fully developing the action rep- 
resentation formalism, and the algorithm, especially the 
part regarding computing assumptions. 
ACKNOWLEDGEMENTS 
For financial support I acknowledge DARPA grant no. 
N0014-90-J-1863 and ARt grant no. DAALO3-89- 
C0031PR1. Thanks to Bonnie Webber for support, in- 
sights and countless discussions, and to all the members 
of the AnimNL group, in particular to Mike White. Fi- 
nally, thanks to the Dipartimento di Informatica - Uni- 
versita' di Torino - Italy for making their computing 
environment available to me, and in particular thanks to 
Felice Cardone, Luca Console, Leonardo Lesmo, and 
Vincenzo Lombardo, who helped me through a last 
minute computer crash. 

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