TENSE GENERATION IN AN INTELLIGENT TUTOR FOR 
FOREIGN LANGUAGE TEACHING: 
SOME ISSUES IN THE DESIGN OF THE VERB EXPERT 
Danilo FUM (*), Paolo Giangrandi(°), Carlo Tasso (o) 
(*) Dipartimento dell~ducazione, Universita' di Trieste, Italy 
(o) Laboratorio di Intelligenza Artificiale, Universita' di Udine, Italy 
via Zanon, 6 - 33100 UDINE, Italy 
e.mail: tasso%uduniv.infn.it@icineca2.bitnet 
ABSTRACT 
The paper presents some of the results 
obtained within a research project aimed at 
developing ET (English Tutor), an intelligent 
tutoring system which supports Italian 
students in learning the English verbs. We 
concentrate on one of the most important 
modules of the system, the domain (i.e. verb) 
expert which is devoted to generate, in a cog- 
nitively transparent way, the right tense for 
the verb(s) appearing in the exercises 
presented to the student. An example which 
highlights the main capabilities of the verb 
expert is provided. A prototype version of ET 
has been fully implemented. 
1. INTRODUCTION 
In the course of its evolution, English has lost 
most of the complexities which still 
characterize other Indo-European languages. 
Modern English, for example, has no 
declensions, it makes minimum use of the 
subjunctive mood and adopts 'natural' gender 
instead of the grammatical one. The 
language, on the other hand, has become 
more precise in other ways: cases have thus 
been replaced by prepositions and fixed word 
order while subtle meaning distinctions can be 
conveyed through a highly sophisticated use 
of tense expressions. Learning correct verb 
usage is however extremely difficult for non 
native speakers and causes troubles to people 
who study English as a foreign language. In 
order to overcome the difficulties which can 
be found in this and several other grammatical 
areas, various attempts have been made to 
utilize Artificial Intelligence techniques for 
developing very sophisticated systems, called 
Intelligent Tutoring Systems, in the specific 
domain of foreign language teaching 
(Barchan, Woodmansee, and Yazdani, 1985; 
Cunningham, Iberall, and Woolf, 1986; 
Schuster and Finin, 1986; Weischedel, Voge, 
and James, 1978; Zoch, Sabah, and Alviset, 
1986). 
An Intelligent Tutoring System (ITS, for 
short) is a program capable of providing 
students with tutorial guidance in a given 
subject (Lawler and Yazdani, 1987; Sleeman 
and Brown, 1982; Wenger, 1987). A full- 
fledged ITS: (a) has specific domain 
expertise; (b) is capable of modeling the 
student knowledge in order to discover the 
reason(s) of his mistakes, and (c) is able to 
make teaching more effective by applying 
different tutorial strategies. ITS technology 
seems particularly promising in fields, like 
language teaching, where a solid core of facts 
is actually surrounded by a more nebulous 
area in which subtle discriminations, personal 
points of view, and pragmatic factors are 
involved (Close, 1981). 
In this paper we present some of the results 
obtained within a research project aimed at 
developing ET (English Tutor), an ITS which 
helps Italian students to learn the English verb 
system. An overall description of ET, of its 
structure and mode of operation has been 
given elsewhere (Fum, Giangrandi, and 
Tasso, 1988). We concentrate here on one of 
the most important modules of the system, the 
domain (i.e. verb) expert which is devoted to 
generate, in a cognitively transparent way, the 
right tense for the verb(s) appearing in the 
exercises presented to the student. The paper 
analyzes some issues that have been dealt 
with in developing the verb expert focusing 
124 - 
on the knowledge and processing mecha- 
nisms utilized. The paper is organized as 
follows. Section two introduces our approach 
to the problem of tense generation in the 
context of a tutor for second language 
teaching. Section three briefly illustrates the 
ET general architecture and mode of 
operation. Section four constitutes the core of 
the paper and presents the design re- 
quirements, knowledge bases and reasoning 
algorithms of the verb expert together with an 
example which highlights its main 
capabilities. The final section deals with the 
relevance of the present proposal both in the 
framework of linguistic studies on verb 
generation and of intelligent tutoring systems 
for language teaching. 
2. THE TENSE GENERATION 
PROBLEM 
An important part of the meaning of a 
sentence is constituted by temporal 
information. Every complete sentence must 
contain a main verb and this verb, in all Indo- 
European languages, is temporally marked. 
The tense of the verb indicates the relation 
between the interval or instant of time in 
which the situation (i.e. state, event, activity 
etc.) described in the sentence takes place and 
the moment in which the sentence is uttered, 
and may also indicate subtle temporal 
relations between the main situation and other 
situations described or referenced in the same 
sentence. Other information can be derived 
from the mood and aspect of the verb, from 
the lexical category which the verb is a 
member of and, more generally, from several 
kinds of temporal expressions that may 
appear in the sentence. Moreover, the choice 
of the tense is determined by other 
information, not directly related with temporal 
meaning, such as speaker's intention and 
perspective, rhetoric characteristics of 
discourse, etc. Very complex relations exist 
among all these features which native 
speakers take into account in understanding a 
sentence or in generating an appropriate tense 
for a given clause or sentence. 
The problem of choosing the right verb tense 
in order to convey the exact meaning a 
sentence is intended to express has aroused 
the interest of linguists, philosophers, logi- 
cians and people interested in computational 
accounts of language usage (see, for example: 
Ehrich, 1987; Fuenmayor, 1987; 
Matthiessen, 1984). There is however no 
agreement on, and no complete theoretical 
account of, the factors which contribute to 
tense generation. The different proposals 
which exist in the literature greatly vary 
according to the different features that are 
actually identified as being critical and their 
level of explicitness, i.e. which features are 
given directly to the tense selection process 
and which must be inferred through some 
form of reasoning 
Our interest in this topic focuses on 
developing a system for tense selection 
capable of covering most of the cases which 
can be found in practice and usable for 
teaching English as a foreign language. A 
basic requirement which we have followed in 
designing ET is its cognitive adequacy: not 
only the final result (i.e. the tense which is 
generated), but also the knowledge and 
reasoning used in producing it should mirror 
those utilized by a human expert in the field 
(i.e. by a competent native speaker). The ITS 
must thus be an 'articulated' or 'glass-box' 
expert. 
3. THE ET SYSTEM 
ET is an intelligent tutoring system devoted to 
support Italian students in learning the usage 
of English verbs. The system, organized 
around the classical architecture of an ITS 
(Sleeman and Brown 1982), consists 
essentially of: 
- the Tutor, which is devoted to manage the 
teaching activity and the interaction with the 
student, 
- the Student Modeler which is able to 
evaluate the student's competence in the 
specific domain, and 
- the Domain (i.e. verb) Expert which is an 
articulated expert in the specific domain dealt 
with by the system. 
In what follows, in order to better understand 
the discussion of the Domain Expert, a 
sketchy account of the system mode of 
operation is given. 
- 125 - 
At the beginning of each session, the Tutor 
starts the interaction with the student by 
presenting him an exercise on a given topic. 
The same exercise is given to the Domain 
Expert which will provide both the correct 
solution and a trace of the reasoning 
employed for producing it. At this point, the 
Student Modeler compares the answer of the 
student with that of the expert in order to 
identify the errors, if any, present in the 
former and to formulate some hypotheses 
about their causes. On the basis of these hy- 
potheses, the Tutor selects the next exercise 
which will test the student on the critical 
aspects pointed out so far and will allow the 
Modeler to gather further information which 
could be useful for refining the hypotheses 
previously drawn. Eventually, when some 
misconceptions have been identified, the 
refined and validated hypotheses will be used 
in order to explain the errors to the student 
and to suggest possible remediations. When a 
topic has been thoroughly analyzed, the Tutor 
will possibly switch to other topics. 
4. THE DOMAIN EXPERT 
The Domain Expert is devoted to generate the 
fight answers for the exercises proposed to 
the student. Usually, exercises are constituted 
by a few English sentences in which some of 
the verbs (open items) are given in infinitive 
form and have to be conjugated into an 
appropriate tense. Sometimes, in order to 
avoid ambiguities, additional information 
describing the correct interpretation (as far as 
the temporal point of view is concerned) of 
the sentence is given. Consequently, the 
Domain Expert must be able: 
i) to select the grammatical tense to employ 
for each open item of the exercise in order to 
correctly describe the status of the world the 
sentence is intended to represent, and 
ii) to appropriately conjugate the verb 
according to the chosen tense. 
Besides these basic functionalities, the 
tutoring environment in which the Domain 
Expert operates imposes a further 
requirement, i.e. the expert must be able: 
iii) to explain to the student how the solution 
has been found, which kind of knowledge 
has been utilized, and why. 
While the sentences that are presented to the 
student are in natural language form, the verb 
expert receives in input a schematic 
description of the sentence. 
Every sentence of the exercise is constituted 
by one or more clauses playing a particular 
role in it (major clauses and minor clauses at 
various levels of subordination). Each clause 
is represented inside the system through a 
series of attribute-value pairs (called exercise 
descriptors) that highlight the information 
relevant for the tense selection process. This 
information includes, for example, the kind of 
clause (main, coordinate, subordinate), 
whether the clause has a verb to be solved, 
the voice and form of the clause, the kind of 
event described by the clause, the time 
interval associated with the event described in 
the clause, etc. Some of the exercise 
descriptors must be manually coded and 
inserted in the exercise data base whereas the 
others (mainly concerning purely linguistic 
features) can be automatically inferred by a 
preprocessor devoted to parsing the exercise 
text. For instance, the schematic description 
of: 
ET > EXERCISE-1: 
7 (live) in this house for ten years. Now the 
roof needs repairing.' 
is the following (with the items automatically 
inferred by the parser preceded by the symbol @): 
EXERCISE: ex 1 
text: 'I (live) in this house for ten years. Now 
the roof needs repairing.' 
@sentence_structure: el, c2 
@clauses to resolve: cl 
CLAUSE: cl 
text: 'I (live) in this house for ten years' 
@clause_kind: main 
@clause_verb: live 
@ superordinate: nil 
@subordinate: nil 
@previous_coordinate: nil 
@clause_form: aff'mnative 
@subject: I 
@ subjecLcase: \[singular first\] 
@voice: active 
@evenLtime: tl 
@time_expression: \['for ten years' t2\] 
- 126 - 
@category: state 
aspect: persistent 
context: informal 
intentionality: nil 
CLAUSE: c2 
TIME_RELATIONS: exl 
meet(t2, now) 
equal(tl, t2). 
When solving an open item, the Domain 
Expert must infer from the exercise 
descriptors all the remaining information 
needed to make the final choice of the 
appropriate tense• This information is 
constituted by several tense features, each one 
describing some facet of the situation that is 
necessary to take into account• The choice of 
which tense features are to be considered in 
the tense selection process represents a 
fundamental step in the design of the verb 
generation module. This problem has no 
agreed upon solution, and it constitutes one of 
the most critical parts of any theory of tense 
generation (Ehrich, 1987; Fuenmayor, 1987; 
Matthiessen, 1984). The main features 
considered by the Domain Expert are listed 
below• Some of the features are already 
included in the exercise descriptors (1 to 4), 
whereas the others must be inferred by the 
system when solving the exercise (5 to 8): 
1. Category, which identifies the kind of 
situation described by the clause (e.g., event, 
state, action, activity, etc.). 
2. Aspect, which concerns the different 
viewpoints that can be utilized for describing 
a situation. 
3. Intentionality, which states whether the 
situation describes a course of action that has 
been premeditated or not. 
4. Context, which concerns the type of 
discourse in which the clause or sentence 
appears. 
5. Duration, which refers to the time span 
(long, short, instantaneous, etc.) occupied by 
a situation. 
6. Perspective, which refers to the position 
along the temporal axis of the situation or to 
its relation with the present time. 
7. Temporal Relations, which refer to the 
temporal relations (simultaneity, contiguity, 
precedence, etc.) that occur between the 
situation dealt with in the current clause and 
the situations described in other clauses• 
8. Adverbial Information, which is related to 
the meaning of possible temporal adverbials 
specified in the same clause. 
The Domain Expert operation is supported by 
a knowledge base constituted by a partitioned 
set of production rules which express in a 
transparent and cognitively consistent way 
what is necessary to do in order to generate a 
verb tense• Its activity is mostly concerned 
with the derivation of the tense features 
strictly related to temporal reasoning. The 
exercise descriptors include for this purpose 
only basic information related to the specific 
temporal adverbials or conjunctions which 
appear in the exercise. This information is 
utilized to build a temporal model of the 
situation described in the exercise. Initially, 
the temporal model is only partially known 
and is then augmented through the application 
of a set of temporal relation rules• This rules 
constitute a set of axioms of a temporal logic - 
similar to that utilized by Allen (1984)- which 
has been specifically developed for: (a) 
representing the basic temporal knowledge 
about the situations described in the exercise; 
(b) reasoning about these knowledge in order 
to compute some of the tense features not 
explicitly present in the schematic description 
of the exercise. The first task of the expert 
module is therefore that of deriving possible 
new relations which hold among situations 
described in the exercise. 
In the schematic description of exercise 1 we 
can see two time relations explicitly asserted: 
meet(d, now) and 
equal(tl, t2). 
The meaning of the fast clause is that the time 
interval t2 (corresponding to the temporal 
expression 'for ten years') precedes and is 
contiguous to the time interval indicated by 
now (i.e. the speaking time)• The meaning of 
the second clause is that the time interval tl 
(representing the state or event expressed by 
the main verb) is equal to the time interval t2. 
From the explick time relation it is possible to 
derive, by employing the following time 
relation rule: 
meet(tx, ty) & equal(tx, tz) => meet(tz, ty). 
the inferred relation: 
127 - 
meet(t1, now). 
The Domain Expert tries then to infer, for 
every exercise clause, the so-called reference 
time, i.e., the moment of time which the 
situation described in the sentence refers to 
(Matthiessen, 1984; Fuenmayor, 1987). In 
order to determine the reference time of every 
clause, the expert utilizes a set of reference 
time identification rules whose condition part 
takes into account the structural description of 
the sentence. 
An example of reference time identification 
rule is the following: 
IF 
1 - clause_kind = main, 
2 - previous_coordinate = nil OR 
new_speaker = nil OR 
clause_form = interrogative, 
3 - time_expression <> nil 
I'HEN 
set the reference_time to the most specific 
time expression 
By applying this rule to the structural 
description of Exercise 1 it is possible to infer 
that the reference time of the clause cl is the 
interval t2 that, being the only time expression 
present in the clause, is also the most specific 
one. 
When all the reference times have been 
determined, the Domain Expert looks only for 
the clauses with open items in order to 
compute (through the temporal axioms) three 
particular temporal relations (Ehrich, 1987): 
deictic (between reference time and speaking 
time: RT-ST), intrinsic (between event time 
and reference time: ET-RT) and ordering 
(between event time and speaking time: ET- 
ST). When these relations have been 
computed, all the needed tense features are 
known, and the final tense selection can be 
performed. Again, a set of selection rules 
takes care of this activity. 
In our example, the following selection rules 
can be applied: 
IF 
I - category = state OR 
category = iterated_action, 
2 - meet(event_time, now), 
3 - meet(reference_time, now), 
4 - equal(event_time, reference_time), 
5 - aspect -- persistent 
THEN 
apply the present perfect tense. 
IF 
1 - category = single_action OR 
category = state, 
2 - meet(evenLtime, now), 
3 - meet(reference_time, now), 
4 - equal(event_time, reference_time), 
5 - duration <> short, 
6 - aspect = persistent, 
7 - context <> formal, 
8 - verb accepts ing_form 
THEN 
apply the present perfect continuous tense. 
which provide two different (both correct) 
solutions for the open item. 
Once the tense to be used has been identified, 
the verb is conjugated utilizing an appropriate 
set of conjugation rules. In our example the 
present perfect is obtained through the 
application, among others, of the following 
rules: 
IF 
tense = present perfect 
THEN 
the verb sequence is formed with: 
- simple present of 'to have' 
- past participle of the verb. 
IF 
1 - tense = past participle, 
2 - verb is regular 
THEN 
the verb sequence is formed with: 
- 'ed-form' of the verb. 
5. CONCLUSIONS 
In the paper we have presented some issues 
involved in the design of a verb generation 
module within a research project aimed at 
developing an ITS capable of teaching the 
English verb system. A first prototype of ET 
has been fully implemented in MRS (LISP 
augmented with logic and rule-programming 
capabilities and with specific mechanism for 
representing meta-knowledge) on a SUN 3 
workstation. 
- 128 - 
Our primary goal in this phase of the project 
has been the cognitive adequacy of the verb 
expert. In order to develop it, we took a 
pragmatic approach, starting with the 
identification of the features traditionally 
considered by grammars, constructing rules 
of tense selection grounded on this features 
and, finally, refining features and rules 
according to the results obtained through their 
use. 
The work presented here relates both to the 
research carried out in the fields of linguistics 
and philosophy, concerning theories of verb 
generation and the temporal meaning of 
verbs, respectively, and the field of intelligent 
tutoring systems. As far as the first topic is 
concerned, we claim that teaching a foreign 
language can constitute a good benchmark for 
evaluating the soundness and completeness of 
such theories. In the field of foreign language 
teaching, on the other hand, the only way to 
build articulated, glass-box experts is to 
provide them with language capabilities such 
as those devised and described by linguistic 
theories. 
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