TRANSLATION BY QUASI LOGICAL FORM TRANSFER 
Hiyan Alshawi, David Carter and Manny P~yner 
SRI International 
Cambridge Computer Science Research Centre 
23 Millers Yard, Cambridge CB2 1RQ, U.K. 
hiyan@cam, sri. com, dmc@cam, sri. com, manny¢cam, sri. com 
BjSrn Gambiick 
Swedish Institute of Computer Science 
Box 1263, S- 164 28 KISTA, Stockholm 
gain@sits, se 
ABSTRACT 
The paper describes work on applying a gen- 
eral purpose natural language processing system 
to transfer-based interactive translation. Trans- 
fer takes place at the level of Quasi Logical Form 
(QLF), a contextually sensitive logical form rep- 
resentation which is deep enough for dealing with 
cross-linguistic differences. Theoretical arguments 
and experimental results are presented to support 
the claim that this framework has good proper- 
ties in terms of modularity, compositionality, re- 
versibility and monotonicity. 
1 INTRODUCTION 
In this paper we describe a translation project 
whose aim is to build an experimental Bilingual 
Conversation Interpreter (BCI) which will allow 
communication through typed text between two 
monolingual humans using different languages (of 
Miike et al, 1988). The choice of languages for the 
prototype system is English and Swedish. Input 
sentences are analysed by the Core Language En- 
gine (CLE 1) as far as the level of Quasi Logical 
Form (QLF; Alshawi, 1990), and then, instead of 
further ambiguity resolution, undergo transfer into 
another QLF having constants and predicates cor- 
responding to word senses in the other language. 
The transfer rules used in this process correspond 
to a certain kind of meaning postulate. The CLE 
then generates an output, sentence from the target 
1 Tile CLE is described in Alshawi (1991) which includes 
more detailed discussion of the BCI architecture in a chap- 
ter by the present, authors, 
language QLF, using the same linguistic data as 
is used for analysis of that language. 
QLFs were selected as the appropriate level for 
transfer because they are far enough removed from 
surface linguistic form to provide the flexibility re- 
quired by cross-linguistic differences. On the other 
hand, the linguistic, unification-based processing 
involved in creating them can be carried out effi- 
ciently and without the need to reason about the 
domain or context; the QLF language has con- 
structs for explicit representation of contextually 
sensitive aspects of interpretation. 
When it is necessary, for correct translation, to 
resolve an ambiguity present at QLF level, the BCI 
system interacts with the source language user to 
make the necessary decision, asking for a choice 
between word sense paraphrases or between alter- 
native partial bracketings of the sentence. There 
• is thus a strong connection between our choice of 
a representation sensitive to context and the use 
of interaction to resolve context dependent ambi- 
guities, but in this paper we concentrate on repre- 
sentational and transfer issues. 
2 CLE REPRESENTATION 
LEVELS 
In this section we explain how QLF fits into the 
overall architecture of the CLE and in section 3 we 
discuss the reasons for choosing it for interactive 
dialogue translation. 
161 
2.1 CLE Processing Phases 
A coarse view of the CLE architecture is that it 
consists of a linguistic analysis phase followed by 
a contextual interpretation phase. The output of 
the first phase is a set of alternative QLF analy- 
ses of a sentence, while the output of the second 
is an RQLF (resolved QLF) representation of the 
interpretation of an utterance: 
Sentence --linguistic analysis--~ QL Fs 
Q X, Fs ---contextual interpretation---*" R Q L F. 
Deriving a fairly conventional Logical Form (LF) 
from the RQLF is then a simple formal mapping 
which removes the information in the RQLF that 
is not concerned with truth conditions. 
Linguistic analysis and contextual interpreta- 
tion each consist of several subphases. For anal- 
ysis these are: orthography, morphological anal- 
ysis, syntactic analysis (parsing), and (composi- 
tional) semantic analysis. Apart from the first, 
these analysis subphases are based on the unifica- 
tion grammar paradigm, and they all use declara- 
tive bidirectional rules. 
When the CLE is being used as an interface to a 
computerized information system (e.g. a database 
system), its purpose is to derive an LF represen- 
tation giving the truth conditions of an utterance 
input by a user. The LF language is based on 
first order predicate logic extended with general- 
ized quantifiers and some other higher order con- 
structs (Alshawi and van Eijck, 1989). For ex- 
ample, in a context where she can refer to Mary 
Smith, and one to "a car", a possible LF for She 
hired one is: 
quant (exists ,C, \[carl ,C\], 
quant (exists ,E, \[event ,El, 
\[past, \[hir • I, E, mary_smith, C\] \] ) ). 
This can be paraphrased as "There is a car C, and 
an event E such that, in the past, ~. is a hiring 
event by Mary Smith of e." In this notation, quan- 
tified formulae consist of a generalized quantifier, 
a variable, a restriction and a scope; square brack- 
ets are used for the application of predicates and 
operators to their arguments. To arrive at such 
LF representations, a number of intermediate lev- 
els of representation are produced by successive 
modular components. 
Generation of linguistic expressions in the CLE 
takes place from QLFs (or from RQLFs by map- 
ping them to suitable QLFs). Since the rules 
used during the analysis phase are declarative and 
bidirectional, these are also used for generation. 
To achieve computationally efficient analysis and 
generation, the rules are pre-compiled in different 
ways for application in the two directions. Gen- 
eration uses the semantic-head driven algorithm 
(Shieber et al, 1990). 
2.2 The QLF Language 
The QLF representations produced for a sen- 
tence are neutral with respect to the choice of ref- 
erents for pronouns and definite descriptions, and 
relations implied by compound nouns and ellip- 
sis. They are also neutral with respect to other 
ambiguities corresponding to alternative scopings 
of quantifiers and operators and to the collec- 
tive/distributive and referential/attributive dis- 
tinctions. The QLF is thus the level of represen- 
tation encoding the results of compositional lin- 
guistic analysis independently of contextually sen- 
sitive aspects of understanding. These aspects 
are addressed by the contextual interpretation 
phase which has the following subphases: quan- 
tifier scoping (Moran 1988), reference resolution 
(Alshawi 1990), and plausibility judgement. 
The QLF language is a superset of the LF 
language containing additional expressions corre- 
sponding, for example, to unresolved anaphors. 
More specifically, there are two additional 
term constructs (anaphoric terms and quanti- 
fied terms), and one additional formula construct 
(anaphoric formulae): 
a_term( Category, Entity Vat, Restriction). 
q_term( Category, Entity Vat, Restriction). 
a_form(Category, Pred Var , Restriction). 
These QLF constructs contain syntactic and 
morphological information in the Category and 
logical (truth-conditional) information in the 
Restriction, itself a QLF formula binding the vari- 
able. A QLF from which the LF for She hired one 
could have been derived is: 
\[past, 
\[hire, 
q_term (<t =quant, n=s ing>, 
E, \[event, E\] ), 
a_term(<t =ref, p=pro, l=she, n=sing>, 
Y, \[female, Y\] ), 
q_t erm (<t =quant, n=sing>, 
C, a_f orm(<t =pred, l=one>, 
P, \[P.C\]))\]\]. 
162 
in which categories are shown as lists of feature- 
value specifications (the feature shown are t for 
QLF expression type, n for number, p for phrase 
type, and 1 for lexical information). The differ- 
ences between the QLF shown here and the LF 
shown earlier are that the quantified terms have 
been scoped, the anaphoric term for she has been 
resolved to Mary Smith, and the anaphoric NP 
restriction implicit in one has been resolved using 
the predicate car. 
The RQLF representation of an utterance in- 
cludes all the information from the QLF, together 
with the resolutions of QLF constructs made dur- 
ing the contextual interpretation phase. For ex- 
ample, the referent of an a_term is unified with 
the a_term variable. 
Some constraints on plausibility can be ap- 
plied at the QLF level before a full interpreta- 
tion has been derived. This is because most of 
the predicate-argument structure of an utterance 
has been determined at that point, allowing, in 
particular, the application of sortal constraints 
expected by predicates of their arguments. Sor- 
tal constraints cut down on structural (e.g. at- 
tachment) ambiguity, and on word sense ambigu- 
ity, the latter being particularly important for the 
translation application in the context of large vo- 
cabularies. 
3 REPRESENTATION LEVELS 
FOR TRANSFER 
The representational structures on which trans- 
fer operates must contain information correspond- 
ing to several linguistic levels, including syntax 
and semantics. For transfer to be general, it must 
operate recursively on input representations. We 
call the level of representation on which this re- 
cursion operates the "organizing" level; semantic 
structure is the natural choice, since the basic re- 
quirement of translation is that it preserves mean- 
ing. 
Syntactic phrase structure transfer, or deep- 
syntax transfer (e.g. Thurmair 1990, Nagao and 
Tsujii 1986) results in complex transfer rules, 
and the predicate-argument structure which is re- 
quired for the application of sortal restrictions is 
not represented. 
McCord's (1988, 1989) organizing level appears 
to be/hat, of surface syntax, with additional deep 
syntactic and semantic content attached to nodes. 
As we have argued, this level is not optimal, which 
may be related to the fact that McCord's sys- 
tem is explicitly not symmetrical: different gram- 
mars are used for the analysis and synthesis of the 
same language, which are viewed as quite differ- 
ent tasks. Isabelle and Macklovitch (1986) argue 
against such asymmetry between analysis and syn- 
thesis on the grounds that, although it is tempting 
as a short-cut to building a structure sufficiently 
well-specified for synthesis to take place, asym- 
metry means that the transfer component must 
contain a lot of knowledge about the target lan- 
guage, with dire consequences for the modularity 
of the system and the reusability of different parts 
of it. In the BCI, however, the transfer rules con- 
tain only cross-linguistic knowledge, allowing the 
analysis and generation to make use of exactly the 
same data. 
Kaplan et al (1989) allow multiple levels of 
representation to take part in the transfer rela- 
tion. However, Sadler et al (1990) point out that 
the particular approach to realizing this taken by 
Kaplan et al has problems of its own and does 
not cleanly separate monolingual from contrastive 
knowledge. 
The CLE processing subphases offer three se- 
mantic representations of different depth as can- 
didates for an appropriate transfer level, namely 
QLF, RQLF and LF. At the LF level, sortal re- 
strictions can be applied, but the form of noun 
phrase descriptions used and also information on 
topicalization is no longer present; the LF rep- 
resentation is too abstract for transfer. On the 
other hand, not all the information appearing in 
the RQLF about how QLF constructs have been 
resolved is necessary for translation. Resolved ref- 
erents are not an adequate generator input for def- 
inite descriptions in the target language, since the 
view of the referent in the source is lost during 
translation. Another case is that translation from 
resolved ellipsis can result in unwieldy target sen- 
tences. In arguing for QLF-level transfer, we are 
asserting that predicate-argument relations of the 
type used in QLF are the appropriate organizing 
level for compositional transfer, while not denying 
the need for syntactic information to ensure that, 
for example, topichood or the given/new distinc- 
tion is preserved. 
Finally, in contrast to systems such as Rosetta 
(Landsbergen, 1986) which depend on stating rule 
by rule correspondences between source and target 
grammars, we wish to make the monolingual de- 
scriptions as independent as possible from the task 
of translating between two languages. Apart from 
163 
its attractions from a theoretical point of view, 
this has practical advantages in allowing gram- 
mars to be reused for different language pairs and 
for applications other than translation. 
4 QLF TRANSFER 
QLF transfer involves taking a QLF analysis of 
a source sentence, say QLF1, and deriving from it 
another expression, QLF2, from which it is possi- 
ble to generate a sentence in the target language. 
Leaving aside unresolved referential expressions, 
the main difference between QLF1 and QLF2 is 
that they will contain constants, particularly pred- 
icate constants, that originate in word sense en- 
tries from the lexicons of the respective languages. 
If more than one candidate source language QLF 
exists, the appropriate one is selected by present- 
ing the user with choices of word sense paraphrases 
and of bracketings relating to differences in the 
syntactic analyses from which the QLFs were de- 
rived. 
A transfer rule specifies a pair of QLF patterns. 
The left hand side matches QLF expressions for 
one language and the right hand side matches 
those for the other: 
trans(<QLFl subexpression pattern> 
<Operator> 
<QLF2 subexpression pattern>). 
If the operator is == then the rule is bidirectional. 
Otherwise, a single direction of applicability is in- 
dicated by use of one of the operators >= or =<. 
Transfer rules are applied recursively, this pro- 
cess following the recursive structure of the source 
QLF. In order to allow transfer between struc- 
turally different QLFs, rules with 'transfer vari- 
ables' need to be used. These variables, which 
take the form tr(atom), show how subexpressions 
in the source QLF correspond to subexpressions 
translating them in the target QLF. For exam- 
ple, the following rule expresses an equivalence 
between the English to be called ("I am called 
John"), and the Swedish beta ("Jag heter John"). 
trans ( \[call_name, 
tr(ev), 
q_term(<tfquant ,n=sing>, 
A, \[entity,A\] ), 
tr(ag), 
tr(name)\] 
\[heCal, Cr (ev), tr (ag), Cr (name) \] ). 
Transfer rules often correspond directly to inter- 
lingual meaning postulates: when the expressions 
in a transfer rule are formulae, the symbols ==, >=, 
and =< can be read as the logical operators <-->, 
-->, and <-- respectively. A rule like 
Crans (\[and, \[bafll ,X\], \[luckl ,X\]\] 
\[otur I, x\] ) 
translating between the English bad luck and the 
Swedish otur, can be interpreted in this way. 
We will now assess the method's strengths and 
weaknesses, as they have manifested themselves in 
practice. We will pay particular attention to the 
criteria of expressiveness, compositionality, sim- 
plicity, reversibility and monotonicity. 
We take the last point first, since it is the most 
straightforward one. Since rules are applied purely 
nondeterministically and by pure unification, we 
get monotonicity "for free" - although there is a 
case for disallowing transfer by decomposition of 
a complex QLF structure which directly matches 
one side of a transfer rule. The other points need 
more discussion. 
4.1 Expressiveness 
Since we are intentionally limiting ourselves by 
not allowing access to full syntactic information 
(but only to that placed in QLF categories) in the 
transfer phase, it is legitimate to wonder whether 
the formalism can really be sufficiently expressive. 
Here, we will attempt to answer this criticism; we 
begin by noting that shortcomings in this area can 
be of several distinct kinds. Sometimes, a formal- 
ism can appear to make it necessary to write many 
rules, where one feels intuitively that one should 
be enough; we treat this kind of problem under the 
heading of compositionality. In other cases, the 
difficulty is rather that there does not appear to 
be any way of expressing the rule at all in terms of 
the given formalism. In our case, a fair proportion 
of problems that at first seem to fall into this cate- 
gory can be eliminated by having adequate mono- 
lingual grammars and using the target grammar 
as a filter; the idea is to allow the transfer com- 
ponent to produce unacceptable QLFs which are 
filtered out by fully constrained target grammars. 
A good example of the use of this technique is 
the English definite article, which in Swedish can 
be translated as a gender-dependent article, but 
preferably is omitted; however, an article is oblig- 
atory before an adjective. Solving this problem 
164 
\[, Table 1: Types of complex transfer used 
Type Example 
Different John likes Mary 
particles John tyeker om Mary 
Passive Insurance is included 
to active FSrs~ikring ingAr 
Verb John owes Mary $20 
to adjective John ~ir skyldig Mary $20 
Support verb John had an accident 
to normal verb John rltkade ut fdr 
en olycka 
Single verb 
to phrase 
Idiomatic 
use of PP 
John wants a car 
John vii1 ha en bil 
(lit.: "wants to have") 
John is in a hurry 
John har br ttom 
(lit.: "has hurry") 
at transfer level is not possible, since the transfer 
component has no way of knowing that a piece of 
logical form will be realized as an adjective; there 
are many cases where an adjective-noun combina- 
tion in English is best translated as a compound 
noun in Swedish. Exploiting the fact that the rele- 
vant constraint is present in the Swedish grammar, 
however, the "transfer-and-filter" method reduces 
the problem to two simple lexical rules. Sortal re- 
strictions at the target end can also be used as a 
filter in a similar way. 
4.2 Simplicity and reversibility 
The most obvious way to put the case with re- 
gard to simplicity is by giving a count of the vari- 
ous categories of rule, and providing evidence that 
there is a substantial proportion of rules which are 
simple in our framework, but would not necessar- 
ily be so in others. 
The transfer component currently contains 718 
rules. 576 of these (80.2%) have the property that 
both the right- and left-hand sides are atomic. 
502 members of this first group (69.9%) translate 
senses of single words to senses of single words; 
the remaining 74 (10.3%) translate atomic con- 
stants representing the senses of complex syntactic 
constructions, most commonly verbs taking parti- 
cles, reflexives, or complementizers. An example 
is the following rule, which defines an equivalence 
between English care about ('John cares about 
Mary") and Swedish bry sig om ( "John bryr sig 
om Mary", lit. "John cares himself about Mary"). 
J Table ~: Transfer contexts used 
'context Example ' 
Perfect tense 
Negated 
John has liked Mary 
John har tyckt om Mary 
John doesn't like Mary 
John tycker inte om Mary 
YN-question Does John like Mary? 
Tycker John om Mary? 
WH-question Who does John like? 
Veto tycker John om? 
Passive Mary was liked by John 
Mary blev omtyckt av John 
Relative The woman that John likes 
clause 
Sentential 
complement 
Embedded 
question 
VP modifier 
Object 
raising 
Change 
of aspect 
Kvinnan som John tycker om 
I think John likes Mary 
Jag tror John tycker om Mary 
I know who John likes 
Jag vet vem John tycker om 
John likes Mary today 
John tycker om Mary idag 
I want John to like Mary 
Jag vill att John ska tycka om 
Mary 
("I want that J. shall like M.") 
John stopped liking Mary 
John slutade tycka om Mary 
("J. stopped like-INF M.") 
trans(care_about == bry_sig_om). 
Since vocabulary has primarily been selected 
with regard to utility (we have, for example, made 
considerable use of frequency dictionaries (Alldn 
1970)), we think it reasonable to claim that QLF- 
based transfer is simplifying the construction of 
transfer rules in a substantial proportion of the 
commonly encountered cases. 
On the score of reversibility, we will once again 
count cases; here we find that 659 (91.8%) of the 
rules are reversible, 17 (2.4%) work only in the 
English-Swedish direction, and 42 (5.8%) only in 
the Swedish-English direction. These also seem to 
be fairly good figures. 
4.3 Compositionality 
As in any rule-based system, "compositionality" 
corresponds to the extent to which it is necessary 
to provide special mechanisms to cover cases of ir- 
regular interactions between rules. As far as we 
know, there is no accepted benchmark for testing 
165 
compositionality of transfer; what we have done, 
as a first step in this direction, is to select six com- 
mon types of complex transfer, and eleven com- 
mon contexts in which they can occur. These are 
summarized in tables 1 and 2 respectively. Each 
complex transfer type is represented by a sample 
rule, as shown in table 1; the question is the ex- 
tent to which the complex transfer rules continue 
to function in the different contexts (table 2). 
To test transfer compositionality properly, it is 
not sufficient simply to note which rule/context 
combinations are handled correctly; after all, it is 
always possible to create a completely ad hoc so- 
lution by simply adding one transfer rule for each 
combination. The problem must rather be posed 
in the following terms: if there is a single rule for 
each complex transfer type, and a number of rules 
for each context, how many extra rules must be 
added to cover special combinations? It is this 
issue we will address. 
The actual results of the tests were as follows. 
There were 124 meaningful combinations (some 
constructions could not be passivized); in 103 of 
these, transfer was perfectly compositional, and no 
extra rule was needed. For example, the English 
sentence for the combination "Verb to adjective + 
WH-question" is How much does John owe Mary. 
The corresponding Swedish sentence is Hut my- 
cket dr John skyldig Mary? ("How much is John 
indebted-to Mary?"), and the two QLFs areS: 
\[uhq, 
\[pres, 
\[owe_have_to_pay, 
q_term(<t=quant,n=sing>,A,\[event,A\]), 
a_term(<t=ref,p=name>, 
B,\[name_of,B,john\]), 
q_term(<t=quant,l=wh>,C,\[quantity,C\]), 
a_term(<t=ref,p=name>, 
D,\[name_of,D,mary\])\]\]\] 
\[whq, 
\[pro8 ent, 
\[Va.T a, 
q_t erm(<t =quanE, n=sing>, A, \[state. A\] ), 
\[skyldiE_nsn_nst, 
a_t erm (<t =ref, p=name>, 
B, \[name_o~, B, j ohn3 ), 
a_term(<t=ref, p=name>, 
C, \[name_of, C,mary\] ), 
q_t erm(<t =quant, l=wh> ,D, \[quantity, D\] )\] \] \] \] 
It should be evident that the complex transfer 
rule defining the equivalence between owe and yarn 
skyldig, 
transC\[owe_have_to_pay, 
q_termC<t=quant,n=sing>,A,\[event,A\]), 
tr(ag),tr(sum),tr(obj)\] 
\[vara, 
q_term(<t=quant,n=sing>,A,\[state,A\]), 
\[skyldig_ngn_ngt, 
trCag),trCobj),tr(sum)\]\]). 
is quite unaffected by being used in the context of 
a Wit-question. 
Of the remaining 21 rule/context/direction 
triples, seven failed for basically uninteresting rea- 
sons: the combination "Perfect tense + Passive- 
to-active" did not generate in English, and the six 
sentences with the object-raising rule all failed in 
the Swedish-English direction due to the transfer 
component's current inability to create a function- 
application from a closed form. The final fourteen 
failures are significant from our point of view, and 
it is interesting to note that all of them resulted 
from mismatches in the scope of tense and nega- 
tion operators. 
The question now becomes that of ascertaining 
the generality of the extra rules that need to be 
added to solve these fourteen unwanted interac- 
tions. Analysis showed that it was possible to 
add 26 extra rules (two of which were relevant 
here), which reordered the scopes of tense, nega- 
tion and modifiers, and accounted for the scope 
differences between the English and Swedish QLFs 
arising from the general divergences in word-order 
and negation of main verbs. These solved ten 
of the outstanding cases. For example, the com- 
bination "Different particles + Negated" is John 
doesn't like Mary in English and John tycker inte 
om Mary (lit.: "John thinks not about Mary") in 
Swedish; the QLF-pair is: 
\[pres p 
\[not, 
\[like, 
q_t erm(<t=quant ,n=sing>, A, \[event, A\] ), 
a_term ( <t=ref, p=name>, 
B, \[name_o~, B, j ohn\] ), 
a_termC<t=ref, p=name>, 
B, \[~ame_of, B ,mary\] )\] \] \] 
2 ~r is the present tense of ~ara. 
166 
\[not, 
\[present, 
\[tycka_om, 
q_t erm(<t =quant, n=s ing>, A, \[event, A\] ), 
a_t erm(<t =ref, p=name>, 
B, \[name_of, B, john\] ), 
a_term(<t=ref, p=name>, 
B, \[name_o:f, B, mary\] ) \] \] \] 
The extra rule here, 
trans( \[pres, \[not,tr(body)\]\] == 
\[not, \[present, tr (body)\] \] ). 
reorders the scopes of the negation and present- 
tense operators, but does not need to access the 
interior structure of the QLF (the "body" vari- 
able); this turns out to be the case for most inter- 
actions of negation, VP-modification and complex 
transfer. It is thus not surprising that a small 
number of similar rules covers most of the cases. 
The four bad interactions left all involved the 
English verb to be; these were the combinations 
"Passive to active ÷ VP modifier" and "Idiomatic 
use of PP q- negation", which failed to transfer 
in either direction. Here, there is no general solu- 
tion involving the addition of a small number of 
extra rules, since the problem is caused by an oc- 
currence of to be on the English side that is not 
matched by an occurrence of the corresponding 
Swedish word on the other. The solution must 
rather be to add an extra rule for each complex 
fransfer rule in the relevan~ class to cover the bad 
interaction. To solve the specific examples in the 
test set, two extra rules were thus required. 
Summarizing the picture, the tests revealed that 
all bad interactions between the transfer rules and 
contexts shown here could be removed by adding 
four extra rules to cover the 124 possible interac- 
tions. In a general perspective (viewing the rules 
as representatives of their respective classes), the 
rule-interaction problems exemplified by the con- 
crete collisions were solved by adding 
• 26 general rules to cover certain standard 
scope mismatches caused by verb-inversion 
and negation. 
• two extra rules (one for present and one for 
past tense) for each complex transfer rule of 
either the "Idiomatic use of PP" or "Active 
to Passive" types, to cover idiosyncratic in- 
teractions of these with negation and VP- 
modification respectively. 
We view these results as very promising: there 
were few bad interactions, and those that ex- 
isted were of a regular nature that could be coun- 
teracted without fear of further unwelcome side- 
effects. This gives good grounds for hoping that 
the system could be scaled up to a practically use- 
ful size without suffering the usual fate of drown- 
ing in a sea of ad hoc fixes. 
5 IMPLEMENTATION STATUS 
The current implementation includes analysis, 
transfer, and generation modules, sizable gram- 
mars with morphological, syntactic and semantic 
rules for English and Swedish, and an experimen- 
tal set of transfer rules for this language pair. Rel- 
ative to the size of the grammars, the lexicons are 
still small (approximately 2000 and 1000 words re- 
spectively). About 250 entries for each language 
have been added for a specific domain (car hire), 
which makes possible moderately unconstrained 
conversation on this topic; the system, including 
the facilities for interactive resolution of trans- 
lation problems, has been tested on a corpus of 
about 400 sentences relating to the domain. For 
short sentences typical of the car hire domain, me- 
dian total processing times for analysis, transfer 
and generation are around ten seconds when run- 
ning under Quintus Prolog on a SUN SPARCst~- 
tion 2. 
We are currently investigating a different QLF 
representation of Iense, aspect and modality which 
should increase the transfer compositionality for 
the operator cases we have discussed in this pa- 
per, as well as allowing more flexible resolution 
of temporal relations in applications other than 
translation. 
ACKNOWLEDGMENTS 
The work reported here was funded by the 
Swedish Institute of Computer Science, and the 
greater part of it was carried out while the third 
author was employed there. We would like to 
thank Steve Pulman for many helpful discussions, 
especially with regard to the problems encoun- 
tered in adapting the English grammar to Swedish. 
167 

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