Resolving Translation Mismatches With Information Flow 
Megumi Kameyama, Ryo Ochitani, Stanley Peters 
The Center for the Study of Language and Information 
Ventura Hall, Stanford University, Stanford, CA 94305 
ABSTRACT 
Languages differ in the concepts and real-world en- 
tities for which they have words and grammatical 
constructs. Therefore translation must sometimes 
be a matter of approximating the meaning of a 
source language text rather than finding an exact 
counterpart in the target language. We propose a 
translation framework based on Situation Theory. 
The basic ingredients are an information lattice, a 
representation scheme for utterances embedded in 
contexts, and a mismatch resolution scheme defined 
in terms of information flow. We motivate our ap- 
proach with examples of translation between En- 
glish and Japanese. 
1 Introduction 
The focus of machine translation (MT) technol- 
ogy has been on the translation of sentence struc- 
tures out of context. This is doomed to limited 
quality and generality since the grammars of un- 
like languages often require different kinds of con- 
textual information. Translation between English 
and Japanese is a dramatic one. The definiteness 
and number information required in English gram- 
mar is mostly lacking in Japanese, whereas the hon- 
orificity and speaker's perspectivity information re- 
quired in Japanese grammar is mostly lacking in 
English. There are fundamental discrepancies in 
the extent and types of information that the gram- 
mars of these languages choose to encode. 
An MT system needs to reason about the context 
of utterance. It should make adequate assumptions 
when the information required by the target lan- 
guage grammar is only implicit in the source lan- 
guage. It should recognize a particular discrepancy 
between the two grammars, and systematically re- 
act to the needs of the target language. 
We propose a general reasoning-based model for 
handling translation mismatches. Implicit informa- 
tion is assumed only when required by the target 
language grammar, and only when the source lan- 
guage text allows it in the given context. Transla- 
tion is thus viewed as a chain of reactive reasoning 
*Linguistic Systems, Fujitsu Laboratories Ltd. 
between the source and target languages. 1 
An MT system under this view needs: (a) a uni- 
form representation of the context and content of 
utterances in discourse across languages, (b) a set of 
well-defined reasoning processes within and across 
languages based on the above uniform representa- 
tion, and (c) a general treatment of translation mis- 
matches. 
In this paper, we propose a framework based on 
Situation Theory (Barwise and Perry 1983). First 
we will define the problem of translation mismatches, 
the key translation problem in our view. Second we 
will define the situated representation of an utter- 
mace. Third we will define our treatment of transla- 
tion mismatches as a flow of information (Barwise 
and Etchemendy 1990). At the end, we will discuss 
a translation example. 
2 What is a translation mismatch? 
Consider a simple bilingual text: 
EXAMPLE I: BLOCKS (an AI problem) 
EWGLISH: 
Consider the blocks world wiCh three blocks, 
A, B, and C. The blocks A and B are on the table. 
C is on A. Which blocks are clear? 
JAPAIIESE: 
mlttu no tumaki A to B to C g~ 6ru tumild no sekLi wo ~ngaete 
three of block A and B and C NOM exist block of world ACC consider 
m/ru 
try 
A to ta no tun~ki ha tnkue no ue 
A and B of block TOPIC t&ble of &bore LOC riding 
C hl A mo ue n| notteiru 
C TOPIC A of .bore LOC riding 
n&nimo ue as nottelnai tam/hi hl dote h 
nothin& above LOC riding block TOPIC which ? 
Note the translation pair C is on A and C t~ A ~9 
_h~j~-~w~ (C ha A no .e ni nofteirn). In En- 
1 Such a reasoning-based MT system is one kind of "negotiation"- 
based system, as proposed by Martin Kay. We thank him for 
stimulating our thinking. 
193 
glish, the fact that C is on top of A is expressed 
using the preposition on and verb is. In Japanese, 
the noun _1= (ue) alone can mean either "on top of" 
or "above", and there is no word meaning just "on 
top of". Thus the Japanese translation narrows the 
relationship to the one that is needed by bringing 
in the verb j~-~ 77 w ~ (notteirn) 'riding'. This phe- 
nomenon of the same information being attached to 
different morphological or syntactic forms in differ- 
ent languages is a well-recognized problem in trans- 
lation. 
TRANSLATION DIVERGENCES 2 of this kind mani- 
fest themselves at a particular representation level. 
They can be handled by (i) STRUCTURE-TO-STRUCTURE 
TRANSFERS, e.g., structural transformations of Na- 
gao (1987), the sublanguage approach of Kosaka et 
al (1988), or by (ii) TRANSFER VIA A "DEEPER" 
COMMON GROUND, e.g., the entity-level of Carbonell 
and Tomita (1987), the lexical-conceptual structure 
of Dorr (1990). A solution of these types is not gen- 
eral enough to handle divergences at all levels, how- 
ever. More general approaches to divergences allow 
(iii) MULTI-LEVEL MAPPINGS, i.e., direct transfer 
rules for mapping between different representation 
levels, e.g., structural correspondences of Kaplan et 
al. (1989), typed feature structure rewriting sys- 
tem of Zajac (1989), and abduction-based system 
of Hobbs and Kameyama (1990). 
We want to call special attention to a less widely 
recognized problem, that of TRANSLATION MISMATCHES. 
They are found when the grammar of one language 
does not make a distinction required by the gram- 
mar of the other language. For instance, English 
noun phrases with COUNT type head nouns must 
specify information about definiteness and number 
(e.g. a town, the town, towns, and the towns are 
well-formed English noun phrases, but not town). 
Whereas in Japanese, neither definiteness nor num- 
ber information is obligatory. Note the translation 
pair Which blocks are clear? and f~ %_h~77 
W~ W~\]~Cg~ ~°~ ( Nanimo ne ni notteinai tnmiki 
ha dore ka) above. Blocks is plural, but tnmiki has 
no number information. 
A mismatch has a predictable effect in each trans- 
lation direction. From English into Japanese, the 
plurality information gets lost. From Japanese into 
English, on the other hand, the plurality informa- 
tion must be explicitly added. 
Consider another example, a portion of step-by- 
step instructions for copying a file from a remote 
system to a local system: 
EXAMPLE 2: FTP 
~Thls term was taken from Dorr (1990) where the prob- 
lem of divergences in verb predicate-argument structures was 
treated. Our use of the term extends the notion to cover a 
much more general phenomenon. 
ENGLISH: 
2. Type 'open', a space, and the name of the 
remote systems and press \[return\]. 
The system displays system connection messages 
and prompts for a user name. 
3. Type the user name for your account on the 
remote system and press \[return\]. 
The system displays a message about passwords 
and prompts for a password if one is required. 
JAPANESE: 
2. open ~1~ ~ ~-- b'~':~-.a,~:~-'l' 7"b~ ~--y 
~o 
'open' kuuhaku rimooto sisutemu met wo taipu si \[RETURN\] 
'open' space remote system name ACC type and \[RETURN\] 
slsntemn setnsokn messeesi to ynnsaa reel wo ton puronputo 
system connection message and user name ACC ash prompt 
ga hyousi s~reru 
NOM display PASSIVE 
rimooto slsutemu deno sihun no ak~unto no yuusa met 
remote system LOC SELF of account of user name 
wo t~ipu s| \[RETURN\] wo osu 
ACC type and \[RETURN\] ACC push 
pasuwaado ni ksnsurn messeess to, moshi pasuwaado Sa 
p~ssword about messaKe And, if password NOM 
hltuyon nara po~suwaado wo tou pronputo ga hyoujl sarern 
required then password ACC ask prompt NOM dlsplay PASSIVE 
The notable mismatches here are the definiteness 
and number of the noun phrases for "space," "user 
name," "remote system," and "name" of the remote 
system in instruction step 2, and those for "mes- 
sage," "password," and "user name" in step 3. This 
information must be made explicit for each of these 
references in translating from Japanese into English 
whether or not it is decidable. It gets lost (at least 
on the surface) in the reverse direction. 
Two important consequences for translation fol- 
low from the existence of major mismatches be- 
tween languages. First in translating a source lan- 
guage sentence, mismatches can force one to draw 
upon information not expressed in the sentence 
information only inferrable from its context at best. 
Secondly, mismatches may necessitate making in- 
formation explicit which is only implicit in the source 
sentence or its context. For instance, the alterna- 
tion of viewpoint between user and system in the 
FTP example is implicit in the English text, de- 
tectable only from the definiteness of noun phrases 
like "a/the user name" and "a password," but Japanese 
grammar requires an explicit choice of the user's 
viewpoint to use the reflexive pronoun zibsn. 
When we analyze what we called translation di- 
vergences above more closely, it becomes clear that 
divergences are instances of lexical mismatches. In 
the blocks example above, for instance, there is a 
mismatch between the spatial relations expressed 
with English on, which implies contact, and Japanese 
194 
ue, which implies nothing about contact. It so hap- 
pens that the verb "notteiru" can naturally resolve 
the mismatch within the sentence by adding the in- 
formation "on top of". Divergences are thus lexical 
mismatches resolved within a sentence by coocur- 
ring lexemes. This is probably the preferred method 
of mismatch resolution, but it is not always possi- 
ble. The mismatch problem is more dramatic when 
the linguistic resources of the target language offer 
no natural way to match up with the information 
content expressed in the source language, as in the 
above example of definiteness and number. This 
problem has not received adequate attention to our 
knowledge, and no general solutions have been pro- 
posed in the literature. 
Translation mismatches are thus a key transla- 
tion problem that any MT system must face. What 
are the requirements for an MT system from this 
perspective? First, mismatches must be made rec- 
ognizable. Second, the system must allow relevant 
information from the discourse context be drawn 
upon as needed. Third, it must allow implicit facts 
be made explicit as needed. Are there any system- 
atic ways to resolve mismatches at all levels? What 
are the relevant parameters in the "context"? How 
can we control contextual parameters in the transla- 
tion process? Two crucial factors in an MT system 
are then REPRESENTATION and REASONING. We 
will first describe our representation. 
3 Representing the translation con- 
tent and context 
Translation should preserve the information con- 
tent of the source text. This information has at least 
three major sources: Content, Context, Language. 
From the content, we obtain a piece of information 
about the relevant world. From the context, we 
obtain discourse-specific and utterance-specific in- 
formation such as information about the speaker, 
the addressee, and what is salient for them. From 
the linguistic forms (i.e., the particular words and 
structures), through shared cooperative strategies 
as well as linguistic conventions, we get information 
about how the speaker intends the utterance to he 
interpreted. 
DISTRIBUTIVE LATTICE OF INFONS. 
In this approach, pieces of information, whether 
• they come from linguistic or non-linguistic sources, 
are represented as infons (Devlin 1990). For an n- 
place relation P, ((P, Zl, ...,z, ;1)) denotes the in- 
formational item, or infon, that zl, ..., xn stand in 
the relation P, and ((P, Zl,...,zn ;0)) denotes the 
infon that they do not stand in the relation. Given 
a situation s, and an infon or, s ~ ~ indicates that 
the infon a is made factual by the situation s, read 
s supports ~r . 
Infons are assumed to form a distributive lattice 
with least element 0, greatest element 1, set I of 
infons, and "involves" relation :~ satisfying: 3 
for infons cr and r, if s ~ cr and cr ~ r 
then s ~ 1- 
This distributive lattice (I, =~), together with a 
nonempty set Sit of situations and a relation ~ on 
Sit x I constitute an infon algebra (see Barwise and 
Etchemendy 1990). 
THE LINGUISTIC INFON LATTICE. We 
propose to use infons to uniformly represent infor- 
mation that come from multiple "levels" of linguis- 
tic abstraction, e.g., morphology, syntax, semantics, 
and pragmatics. Linguistic knowledge as a whole 
then forms a distributive lattice of infons. 
For instance, the English words painting, draw- 
ing, and picture are associated with properties; call 
them P1, P2, and P3, respectively. In the following 
sublattice, a string in English (EN) or Japanese(JA) 
is linked to a property with the SIGNIFIES relation 
(written ==),4 and properties themselves are inter- 
linked with the INVOLVES relation (=~): 
EN: "picture" ~-= Pl((picture, x; 1)) 
EN: "painting" == P2((painting, x; 1)) 
EN: "drawing" == P3((drawing, x; 1)) 
EN: "oil painting" =----- P4((oil painting, x; 1~ 
EN: "water-color" == Ph((water-color, x; 1)) 
P2 ¢> P1, P3 ~ P1, P4 =P P2, PS =P P2 
So far the use of lattice appears no different from 
familiar semantic networks. Two additional factors 
bring us to the basis for a general translation frame- 
work. One is multi-linguality. The knowledge of 
any new language can be added to the given lattice 
by inserting new infons in appropriate places and 
adding more instances of the "signifies" relations. 
The other factor is grammatical and discourse-functional 
notions. Infons can be formed from any theoretical 
notions whether universal or language-specific, and 
placed in the same lattice. 
Let us illustrate how the above "picture" sublat- 
tice for English would be extended to cover Japanese 
words for pictures. In Japanese, ~ (e) includes both 
paintings and drawings, but not photographs. It is 
thus more specific than picture but more general 
than painting or drawing. No Japanese words co- 
signify with painting or drawing, but more specific 
concepts have words-- ~ (aburae) for P4, 
(suisaiga) for P5, and the rarely used word ~ (senbyou) 
for artists' line drawings. Note that syn- 
onyms co- signify the same property. (See Figure 1 
for the extended sublattice.) 
3We assmne that the relation =~ on infons is transitive, 
reflexive, and anti-symmetric after Barwise and Etchemendy. 
4This is our addition to the infon lattice. The SIGNIFIES 
relation links the SIGNIFIER and SIGNIFIED to forrn a SIGN (de 
Saussure 1959). Our notation abbreviates standard infons, 
e.g., ((signifies, "picture", EN, P1; 1)) . 
195 
EN:"picture n m---- P1 ((picture,x; I)) 
n 
EN:-p~intins~ JA:'e m m--.-- P6 ((e,z; 1)) 
((p~tnting,z; 1))P2 P3 ((drawlns,x;l))----~ EN:Sdrawing" 
R> P7 ((line dr&wing,z;1)) 
({oil p&intins,~c; 1))P4 P5 ((water.colorjc;1)) %% JA:asenbyou ~ 
EN:~oil p~nting j EN:aw&ter.color j 
JA:U&burae" JA: =suis~lga = 
Figure 1: The "Picture" Sublattice 
((give, x, y, .;i)) 
^ ((pov, x;l)) 
^({look-up, s, s; 0)) 
^((look-down, s, m;0)) 
^((speaker, s, 1)) 
((give, z, y, s;1)) 
^((pov, s;l)) 
A((looLup, s, x;0)) 
^((look-down, a, x;O)) 
^((spe6ker, s;l)) 
({give, x, y, s;l)) ((give, z, y, s;l)) 
^((po., ffi;l)) ^((pov, x;z)) 
^((look.up, s, s;1)) ^((look-up, s, s;0)) 
^((look-down, s, I;O)) ^((look.down, s, s;1)) 
^((.p.~ker, .~11) 
__~--_ JA-.Ukudas~ru~'"~ ~..~ JA:~yokosu N 
((~.~,  , y, .;1)) ((gi.~, =, ~, .;1)) ^((pov, s~s)) ^((~o., J;s)) 
^((look-up, s, x;1)) ^((look.up, s, x;0)) 
^((look.down, s, x; 0)) ^((look-down, s, x;1)) 
^((speaker s;l)) ^((speaker 8;1)) 
Figure 2: Verbs of giving 
JA: "Jr(e)" == PO((e, x; 1)) 
JA: "~l~(aburae)" == P4({oil painting, x; I}) 
JA: "f#L~iU(muisaiga)" ----= PS((water-color, x; 1)) 
JA: "W/~/l(senbyou)" ----= P7{(senbyou, x; I}) 
P2 =~ P6, P3 =P PS, PS =~ PI, P7 =#P P3 
Lexical differences often involve more complex prag- 
matic notions. For instance, corresponding to the 
English verb give, Japanese has six basic verbs of 
giving, whose distinctions hinge on the speaker's 
perspectivity and honorificity. For "X gave Y to Z" 
with neutral honorificity, ageru has the viewpoint 
on X, and burets, the viewpoint on Z. Sasiageru 
honors Z with the viewpoint on X, and l~udasaru 
honors X with the viewpoint on Z, and so on. See 
Figure 2. 
As an example of grammatical notions in the lat- 
tice, take the syntactic features of noun phrases. 
English distinguishes six types according to the pa- 
rameters of count/mass, number, and definiteness, 
whereas Japanese noun phrases make no such syn- 
tactic distinctions. See Figure 3. Grammatical no- 
tions often draw on complex contextual properties 
such as "definiteness", whose precise definition is a 
research problem on its own. 
THE SITUATED UTTERANCE REPRE- 
SENTATION. A translation should preserve as 
far as practical the information carried by the source 
text or discourse. Each utterance to be translated 
gives information about a situation being described-- 
precisely what information depends on the context 
in which the utterance is embedded. We will utilize 
what we call a SITUATED UTTERANCE REPRESEN- 
TATION (SUR) to integrate the form, content, and 
~N~ UN~:JA 
=;0)) 
Figure 3: The "NP" Sublattice 
context of an utterance. 5 In translating, contextual 
information plays two key roles. One is to reduce 
the number of possible translations into the target 
language. The other is to support reasoning to deal 
with translation mismatches. 
Four situation types combine to define what an 
utterance is: 
Described Situation The way a certain piece of 
reality is, according to the utterance 
Phrasal Situation The surface form of the utter- 
ance 
Discourse Situation The current state of the on- 
going discourse when the utterance is produced 
Utterance Situation The specific situation where 
the utterance is produced 
The content of each utterance in a discourse like 
the Blocks and FTP examples is that some situa- 
tion is described as being of a certain type. This 
is the information that the utterance carries about 
the DESCRIBED SITUATION. 
The PHRASAL SITUATION represents the surface 
form of an utterance. The orthographic or phonetic, 
phonological, morphological, and syntactic aspects 
of an utterance are characterized here. 
The DISCOURSE SITUATION is expanded here in 
situation theory to characterize the dynamic as- 
pect of discourse progression drawing on theories 
in computational discourse analysis. It captures 
the linguistically significant parameters in the cur- 
rent state of the on-going discourse, s and is espe- 
cially useful for finding functionally equivalent re- 
ferring expressions between the source and target 
languages. ¢ 
• reference time = the time pivot of the linguistic 
SOur characterization of the context of utterance draws 
on a number of existing approaches to discourse representa- 
tion and discourse processing, most notably those of Grosz 
and Sidner (1986), Discourse Representation Theory (Kamp 
1981, Helm 1982), Situation Semantics (Barwise and Perry 
1983, Gawron and Peters 1990), and Linguistic Discourse 
Model (Scha and Polanyi 1988). 
°Lewis (1979) discussed a number of such parameters in 
a logical framework. 
7Different forms of referring expressions (e.g. pronouns, 
demonstratives) and surface structures (i.e. syntactic and 
196 
description ("then") s 
• point of view = the individual from whose view- 
point a situation is described ~ 
• attentional state -- the entities currently in the 
focus and center of attention ~° 
• discourse structural context = where the utter- 
ance is in the structure of the current discourse I z 
The specific UTTERANCE SITUATION contains in- 
formation about those parameters whose values sup- 
port indexical references and deixes: e.g., informa- 
tion about the speaker, hearer(s), the time and loca- 
tion of the utterance, the perceptually salient con- 
text, etc. 
The FTP example text above describes a situation 
in which a person is typing commands to a com- 
puter and it is displaying various things. Specif- 
ically, it describes the initial steps in copying a 
file from a remote system to a local system with 
ftp. Consider the first utterance in instruction step 
~uttering, x, u, t; 1 ~ ^ ~addressing, ~, y, t; 1 
Note that the parameter y of DeS for the user 
(to whom the discourse is addressed) has its value 
constrained in US; the same is true of the param- 
eter t for utterance time. Similarly, the parameter 
r of DeS for the definite remote system under dis- 
cussion is assigned a definite value only by virtue of 
the information in DiS that it is the unique remote 
system that is salient at this point in the discourse. 
This cross-referencing of parameters between types 
constitutes further support for combining all four 
situation types in a unified SUR. In order for the 
analysis and generation of an utterance to be as- 
sociated with an SUIt, the grammar of a language 
should be a set of constraints on mappings among 
the values assigned to these parameters. 
4 Translation as information flow 
3 repeated here: Type the user name for your .... d , - ~ Translation must often be a matter of approxi- 
accoun~ on ~ne remo~e system an press Lre~urnj .......... 
It occurs in a type of DISCOURSE SITUATION where mating the meaning oI a source mnguage ~ex~ ramer than finding an exact counterpart in the target lan- 
there has previously been mention of a remote sys- 
tem and where a pattern has been established of 
alternating the point of view between the addressee 
and another agent (the local computer system). We 
enumerate below some of the information in the 
SUl~ associated with this utterance. The Described Situation (DES) of the utterance is 
~type, y,n,t~;1 ~ A ~press, y,k,tl~;1 ~ where n 
satisfies n = n I ~=~ ~named, a, n~; 1 ~ a satisfies 
~account, a, y,r; 1 ~ r satisfies ~system, r; 1 
A ~'~remotefrom, r,y;1 ~tlsatisfies~later, t~,t;1 ~'n , 
k satisfies ~named,k,\[return\];l~ t satisfies ~later, t , t ; 1 
The Phrasal Situation (PS) of the utterance is 
~language, u,English; 1 ~ ^ ~written, u, "Type the 
user name for your account on the remote system and 
press \[return\]."; 1 ~ ^ ~syntax, u,{...~written, e, "the 
user name"; 1 ~ ^ ~np, e; 1 ~ ^ ~deflnite, e; 1 ~, 
A ~singular, e; 1 ~ ^ ...}; 1 
The Discourse Situation (DIS) is 
r = r ~ ~ ~focus, el,remote system; 1 ~, 
Finally, the Utterance Situation (US) is 
phonetic) often carry equivalent discourse functions, so ex- 
plicit discourse representation is needed in translating these 
forms. See also Tsujil (1988) for this point. 
s Reichenb~.h (1947) pointed out the significance of refer- 
ence time, which in the FTP example accounts for why the 
addressee is to press \[return\] after typing the user name of 
his/her remote a~count. 
9 Katagiri (to appear) describes how this parameter inter- 
acts with Japanese grammar to constrain use of the reflexive 
pronoun zibu~. 
10 See Grosz (1977), Grosz et al. (1983), Kameyama (1986), 
Brennan et al. (1987) for discussions of this parameter. 
llThis parameter may be tied to the "intentional" aspect 
of discourse as proposed by Grosz and Sidner (1986). See, 
e.g., Scha and Polanyi (1988) and Hobbs (1990) for discourse 
structure models. 
guage since languages differ in the concepts and 
real-world entities for which they have words and 
grammatical constructs. 
In the cases where no translation with exactly the 
same meaning exists, translators seek a target lan- 
guage text that accurately describes the same real 
world situations as the source language text. 12 The 
situation described by a text normally includes ad- 
ditional facts besides those the text explicitly states. 
Human readers or listeners recognize these addi- 
tional facts by knowing about constraints that hold 
in the real world, and by getting collateral informa- 
tion about a situation from the context in which a 
description is given of it. For a translation to be 
a good approximation to a source text, its "fleshed 
out" set of facts--the facts its sentences explicitly 
state plus the additional facts that these entail by 
known real-world constraints--should be a maximal 
subset of the "fleshed out" source text facts. 
Finding a translation with the desired property 
can be simplified by considering not sets of facts 
(infons) but infon lattices ordered by involvement 
relations including known real-world constraints. If 
a given infon is a fact holding in some situation, 
all infons in such a lattice higher than the given 
one (i.e., all further infons it involves) must also 
be facts in the situation. Thus a good translation 
can be found by looking for the lowest infons in the 
lattice that the source text either explicitly or im- 
plicitly requires to hold in the described situation, 
and finding a target language text that either ex- 
plicitly or implicitly requires the maximal number 
12In some special cases, translation requires mapping be- 
tween different hut equivalent real world situations, e.g., cars 
drive on different sides of the street in Japan and in the US. 
197 
of them to hold. 13 
THE INFORMATION FLOW GRAPH. Trans- 
lation can be viewed as a flow of information that re- 
sults from the interaction between the grammatical 
constraints of the source language (SL) and those 
of the target language (TL). This process can be 
best modelled with information flow graphs (IFG) 
defined in Barwise and Etchemendy 1990. An IFG 
is a semantic formalization of valid reasoning, and is 
applicable to information that comes from a variety 
of sources, not only linguistic but also visual and 
other sensory input (see Barwise and Etchemendy 
1990b). By modelling a treatment of translation 
mismatches with IFGs, we aim at a semantically 
correct definition that is open to various implemen- 
tations. 
IFGs represent five basic principles of information 
flow: 
Given Information present in the initial assump- 
tions, i.e., an initial "open case." 
Assume Given some open case, assume something 
extra, creating an open subcase of the given 
case. 
Subsume Disregard some open case if it is sub- 
sumed by other open cases, any situation that 
supports the infons of the subsumed case sup- 
ports those of one of the subsuming cases. 
Merge Take the information common to a number 
of open cases, and call it a new open case. 
Recognize as Possible Given some open case, rec- 
ognize it as representing a genuine possibility, 
provided the information present holds in some 
situation. 
RESOLVING MISMATCHES. First~ a trans- 
lation mismatch is recognized when the generation 
of a TL string is impossible from a given set of in- 
fons. More specifically, 
given a Situated Utterance Representation 
(SUIt), when no phrasal situations of TL 
support SUR because no string of TL sig- 
nifies infon a in SUR, The TL grammar 
cannot generate a string from SUR, and 
there is a TRANSLATION MISMATCH on 0 r. 
A translation mismatch on ~, above is resolved in 
one of two directions: 
Mismatch Resolution by Specification: 
Assume a specific case r such that r =:~ 
and there is a Phrasal Situation of TL that 
supports v. A new open case SUR' is then 
generated, adding r to SUR. 
13As more sophisticated translation is required, We could 
make use of the multiple situation types to give more impor- 
tance to some aspects of translation than others depending 
on the purpose of the text (see Hauenschild (1988) for such 
translaion needs). 
This is the case when the Japanese word ~ (e) is 
translated into either painting or drawing in English. 
The choice is constrained by what is known in the 
given context. 
Mismatch Resolution by Generaliza- 
tion: Assume a general case r such that a 
=~ r and there is a Phrasal Situation of TL 
that supports r. A new open case SUR' is 
then generated, adding 7- to SUR. 
This is the case when the Japanese word ~ (e) is 
translated into picture in English, or English words 
ppainting and drawing are both translated into 
(e) in Japanese. That is, two different utterances 
in English, I like this painting and I like this draw- 
ing, would both be translated into ~J~l'~ ~ Otl~ff 
~'~ (watasi wa kono e ga suki desn) in Japanese 
according to this scheme. 
Resolution by generalization is ordinarily less con- 
strained than resolution by specification, even though 
it can lose information. It should be blocked, how- 
ever, when generalizing erases a key contrast from 
the content. For example, given an English utter- 
ance, I like Matisse's drawings better than paintings, 
the translation into Japanese should not generalize 
both drawings and paintings into ~ (e) since that 
would lose the point of this utterance completely. 
The mismatches must be resolved by specification in 
this case, resulting in, for instance, $J~1"~'¢~" 4 gO 
~tt~e~A~ \]: 9 ~ ~t~ ~'t?'J" ( watasi wa Ma- 
tisse no abnrae ya snisaiga yorimo senbyou ga suki 
dest 0 'I like Matisse's line_drawings(P7) better than 
oil_paintings(P4) or water-colors(P5)'. 
There are IFGs for the two types of mismatch 
resolution. Using o for an open (unsubsumed) node 
and • for a subsumed node, we have the following: 
Mismatch Resolution by Specification: (given r :~ a) 
Given: o{a} Assume: ?{a} 
/ 6{¢, ~} 
Mismatch Resolution by Generalization: (given o" :¢, ¢) 
Given: o{a} Assume: l{a} Subsume: l{a} 
6{¢,¢} ~{q,T} 
Both resolution methods add more infons to the 
given SUR by ASSUMPTION, but there is a differ- 
ence. In resolution by specification, subsequent sub- 
surnption does not always follow. That is, only by 
contradicting other given facts, can some or all of 
the newly assumed SUR's later be subsumed, and 
only by exhaustively generating all its subcases, the 
original SUR can be subsumed. In resolution by 
generalization, however, the newly assumed general 
case immediately subsumes the original SUR. 14 
14Resolution by specification models a form of abductive 
inference, and generalization, a form of deductive inference 
198 
Source Language Target L~ngu@ge 
Discourse 
Situations 
DiS 1 .. DiS m 
Utterance 
Situations 
US 1 .. USI 
Phrasal 
Situations 
PS 1 .. PS k 
Discourse 
Situations 
~is i .. Dis~, 
Utterance 
Situations 
~s i .. ~s i, 
Phrasal 
Situations 
Psi .. Psi,, 
Figure 4: Situated Translation 
THE TRANSLATION MODEL. Here is our 
characterization of a TRANSLATION: 
Given a SUR ( DeT, PS, DiS, US ) of 
the nth source text sentence and a dis- 
course situation DiS" characterizing the 
target language text following translation 
of the (n-1)st source sentence, find a SUR 
( DeT', PS ~, DiS ~, US ~) allowed by the tar- 
get language grammar such that DiS" _C 
DiS ~ and 
( DeT, PS, DiS, US ) ,~ ( DeT s, PS s, DiS ~, US'). 
(N is the approximates relation we have 
discussed, which constrains the flow of in- 
formation in translation.) 
Our approach to translation combines SURs and 
IFGs (see Figure 4). Each SUR for a possible inter- 
pretation of the source utterance undergoes a FLOW 
OF TRANSLATION as follows: A set of infons is ini- 
tially GIVEN in an SUR. It then grows by mismatch 
resolution processes that occur at multiple sites un- 
til a generation of a TL string is RECOGNIZED AS 
POSSIBLE. Each mismatch resolution involves AS- 
SUMING new SUR's and SUBSUMING inconsistent or 
superfluous SUR's. ~s 
Our focus here is the epistemologicai aspect of 
translation, but there is a heuristically desirable 
property as well. It is that the proposed mismatch 
resolution method uses only so much additional in- 
formation as required to fill the particular distance 
between the given pair of linguistic systems. That 
is, the more similar two languages, leas computa- 
tion. This basic model should be combined with 
various control strategies such as default reasoning 
in a sltuation-theoretic context. One way to implement these 
methods is in the abduction-based system proposed by Hobbs 
and Kameyama (1990). 
~SA possible use of MERGE in this application is that two 
different SUit's may be merged when an identical TL string 
would be generated from them. 
((count,.x.zx)) ~p5 ((de~,x;o)) 
Uthe user name N athe user n&mes u ua user name ~ ~user nlLmesn 
Figure 5: The IFG for NP Translation 
in an actual implementation. 
5 A translation example 
We will now illustrate the proposed approach with 
a Japanese-to-English translation example: the first 
sentence of instruction step 3 in the FTP text. 
INPUT STRING: "3. ~ -~'-- \]- ":/.~ ~'J-~'C'~'J ~'ff)7" 
~/~=---~~" 7"L~ ~--y~9-o " 
1. In the initial SUR are infons for 9 -~-- b ":I ~ ~" 
(rimoofo sisutemu) 'remote system', 7' ~ 
:I i. (akaunfo) 'account', and :'---'~ (yu~zaa 
mei) 'user name'. All of thesewords signify 
properties that are signified by English COUNT 
nouns but the Japanese SUR lacks definiteness 
and number information. 
2. Generation of English from the SUR fails be- 
cause, among other things, English grammar 
requires NPs with COUNT head nouns to be of 
the type, Sg-Def, Sg-Indef, PI-Def, or Pl-Indef. 
(translation mismatch) 
3. This mismatch cannot be resolved by general- 
ization. It is resolved by assuming four sub- 
cases for each nominal, and subsuming those 
that are inconsistent with other given informa- 
tion. The "remote system" is a singular entity 
in focus, so it is Sg-Def, and the other three 
subcases are subsumed. The "user name" is 
an entity in center, so Definite. The "account" 
is Definite despite its first mention because its 
possesser (addressee) is definite. Both "user 
name" and "account" can be either Singular or 
Plural at this point. Let's assume that a form 
of default reasoning comes into play here and 
concludes that a user has only one user name 
and one account name in each computer. 
4. The remaining open case permits generation of 
English noun phrases, so the translation of this 
utterance is done. 
OUTPUT STRING: "Type the user name for your 
account on the remote system and ..." 
6 Conclusions 
In order to achieve high-quality translation, we 
need a system that can reason about the context of 
utterances to solve the general problem of transla- 
199 
tion mismatches. We have proposed a translation 
framework based on Situation Theory that has this 
desired property. The situated utterance represen- 
tation of the source string embodies the contextual 
information required for adequate mismatch reso- 
lution. The translation process has been modelled 
as a flow of information that responds to the needs 
of the target language grammar. Reasoning across 
and beyond the linguistic levels, this approach to 
translation respects and adapts to differences be- 
tween languages. 
7 Future implications 
We plan to design our future implementation of 
an MT system in light of this work. Computational 
studies of distributive lattices constrained by multi- 
ple situation types are needed. Especially useful lin- 
guistic work would be on grammaticized contextual 
information. More studies of the nature of transla- 
tion mismatches are also extremely desirable. 
The basic approach to translation proposed here 
can be combined with a variety of natural language 
processing frameworks, e.g., constraint logic, ab- 
duction, and connectionism. Translation systems 
for multi-modal communication and those of multi- 
ple languages are among natural extensions of the 
present approach. 
8 Acknowledgements 
We would like to express our special thanks to 
Hidetoshi Sirai. Without his enthusiasm and en- 
couragement at the initial stage of writing, this pa- 
per would not even have existed. This work has 
evolved through helpful discussions with a lot of 
people, most notably, Jerry Hobb8, Yasuyoshi Ina- 
gaki, Michio Isoda, Martin Kay, Hideo Miyoshi, Hi- 
roshi Nakagawa, Hideyuki Nakashima, Livia Polanyi, 
and Yoshihiro Ueda. We also thank John Etchemendy, 
David Israel, Ray Perrault, and anonymous review- 
ers for useful comments on an earlier version. 

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