Coreference-oriented Interlingual Slot Structure & Machine 
Translation, 
Jes6s Peral, Manuel Palomar and Antonio Ferrdndez 
Research Group on Language Processing and Information Systems. 
Department of Software and Computing Systems. University of Alicante 
03690 San Vicente del Raspeig. Alicante, Spain, 
{jperal, mpalomar, antonio}@dlsi.ua.es 
Abstract 
One of the main problems of many 
commercial Machine Translation (MT) and 
experimental systems is that they do not carry 
out a correct pronominal anaphora generation. 
As mentioned in Mitkov (1996), solving the 
anaphora and extracting the antecedent are 
key issues in a correct translation. 
In this paper, we propose an Interlingual 
mechanism that we have called lnterlingual 
Slot Structure (ISS) based on Slot Structure 
(SS) presented in Ferrfindez et al. (1997). The 
SS stores the lexical, syntactic, morphologic 
and semantic information of every constituent 
of the grammar. The mechanism 1SS allows us 
to translate pronouns between different 
languages. In this paper, we have proposed 
and evaluated ISS for the translation between 
Spanish and English languages. We have 
compared pronominal anaphora resolution 
both in English and Spanish to accomplish a 
study of the existing discrepancies between 
two languages. 
This mechanism could be added to a MT 
system such as an additional module to solve 
anaphora generation problem. 
Introduction 
According to Mitkov (1996), the establishment of 
the antecedents of anaphora is of crucial 
importance for a correct translation. It is essential 
to solve the anaphoric relation when a language is 
translated into one that marks the pronoun gender. 
On the other hand, anaphora resolution is vital 
when translating discourse rather than isolated 
sentences since the anaphoric references to 
preceding discourse entities have to be identified. 
Unfortunately, the majority of Machine 
Translation (MT) systems do not deal with 
anaphora resolution and their successful operation 
usually does not go beyond the sentence level. 
Another important aspect in automatic translation 
of pronouns, as mentioned in Mitkov (1996), 
consists on the application of two possible 
techniques: translation or reconstruction of 
referential expressions. 
In the first technique, source language pronouns 
are directly translated into target language 
pronouns without studying their relation with 
other words in the text. 
The second technique considers that the pronouns 
are not autonomous in their meaning/function but 
dependent on other units in the text. Then, a more 
natural way to treat pronouns in MT would be the 
following: (a) analysis has to determine the 
reference structure of the source text, i.e. 
coreference/cospecification relationships between 
anaphora and antecedents have to be determined, 
(b) this is the only information that is conveyed to 
the target language generator, (c) the target 
language generator generates the appropriate 
target language surface expression as a function of 
the target equivalent of the source antecedent 
and/or according to the rules of this language. 
Mitkov et al. (1995) adopt a similar approach. 
In this work, we present an Interlingual (formal 
language without' ambiguity) mechanism proposal 
based on the second technique. Basically, a 
structure that stores the anaphora and its 
antecedent in the source language is used. From 
this structure, a similar one in the target language 
is generated. Using this new structure we will be 
able to generate the final surface structure of the 
original sentence. 
I This paper has been supported by the CICYT number TIC97-0671-C02-02. 
69 
In the following section we will describe the 
general purpose anaphora resolution system. The 
following section will show the anaphora 
resolution module, where we will focus on the 
differences between English and Spanish system 
and we will report some evaluation results. After 
that, we will present our Interlingual mechanism 
based on the English-Spanish discrepancy 
analysis. Finally, we will discuss the evaluation of 
some commercial MT systems with their 
problems in pronouns translation and we will 
study the solution with our proposal. 
1 General purpose anaphora resolution 
system 
The general purpose anaphora resolution system 
with our Interlingual module is shown in 
Figure 1. It can be observed that there are two 
processes in parallel, corresponding to anaphora 
resolution in English and Spanish. These two 
processes are independent of each other and they 
are connected by means of the Interlingual 
mechanism. The input of each process is a 
grammar defined by means of the grammatical 
formalism SUG (Slot Unification Grammar) 
Ferrfindez et al. (1997), Ferrhndez (1998a). A 
translator which transforms rules SUG into Prolog 
clauses has been developed. This translator will 
provide a Prolog program that will parse each 
sentence. This system can carry out a partial or 
full parsing of the text with the same parser and 
grammar. In this paper we will use a partial 
parsing (Slot Unification Partial Parser, SUPP). 
This partial parser SUPP described in 
Martinez-Barco et al. (1998), works on 
unrestricted corpus that contains the words tagged 
with their obtained grammatical categories from 
the output of a "part-of-speech (POS) tagger". In 
this paper, we have" used bilingual corpus (Blue 
Book, English and Spanish) CRATER (1994) for 
the evaluation of anaphora resolution module. 
The output of the parsing module will be what we 
have called Slot Structure (henceforth SS) that 
stores the necessary information for linguistic 
phenomena resolution. This SS will be the input 
for the following module in which we deal with 
anaphora resolution as well as other linguistic 
phenomena (extraposition, ellipsis, ...). 
After applying the linguistic phenomena 
resolution algorithm we obtain a new slot 
structure (SS) that will store both the anaphora 
and their antecedents. This new structure in the 
source language will be the input for the 
Interlingual mechanism (Interlingual Slot 
Structure, ISS), which will obtain the 
corresponding slot structure in the target 
language. Using this new structure we will be able 
to generate the final surface structure of the 
original sentence. 
Syntactic analysis \] Syntactic analysis module 
(SUPP) module (SUPP) 
Slot Structure(SS) I Slot Structure(SS) 
Anaphora resolution module \[ Anaphora resolution module 
SS'without~aphora \] ~ SS'withoutanaphora 
Figure 1. 
2 The anaphora resolution module 
In this section we will describe the anaphora 
resolution module of our system. This section 
consists of two subsections. In the first one we 
show the algorithm for anaphora resolution. Next, 
we show the evaluation of the module. 
2.1 The algorithm 
We are going to describe an algorithm that deals 
with discourse anaphora in unrestricted texts 
using partial or full parsing. It is based on the 
process described in Figure 1. So, this process 
will be applied after the parsing of a sentence. 
This algorithm is shown in Figure 2 and it can 
deal with pronominal anaphora, surface-count 
anaphora and one-anaphora as is shown in 
Ferrfindez (1998a). This algorithm will use a Slot 
Structure (SS) corresponding to the output of the 
parsing module and a list of antecedents. This list 
consists of the slot structures of all the previously 
parsed noun phrases. For each anaphor in this SS, 
several constraints and preferences will be 
applied. The output of this algorithm consists on a 
70 
new SS (SS), where each anaphor has been stord~d 
with its correct antecedent. 
Parse a sentence. We obtain its slot structure (SS1). 
For each anaphor in SSI : 
Select the antecedents of the previous X sentences 
depending on the kind of anaphor in LO 
Apply constraints (depending on the kind of anaphor) to LO 
with a result of Ll : 
Case of." 
ILl1 = 1 Then: 
This one will be the antecedent of the anaphor 
ILII > 1 Then: 
Apply preferences (depending on the kind of anaphor) to 
LI, with a result of L2: 
• The first one of L2 will be the selected antecedent 
Update SSI with each antecedent of each anaphor with a result of SS2. 
Figure 2. 
The detection of the anaphors and possible 
antecedents is easily carried out by means of the 
information stored in each SS, i.e. its functor and 
arity. For example, the antecedents have an SS 
with np as their functor, whereas the pronouns 
have pron. We have considered the previous two 
sentences to search for antecedents of a pronoun. 
The algorithm will apply a set of constraints 
(morphosyntactic agreement and c-command 
constraints) to the list of possible antecedents in 
order to discount candidates. If there is only one 
candidate, this one will be the antecedent of the 
anaphor. Otherwise, if there is still more than one 
candidate left, a set of preferences (syntactic 
parallelism, lexical information, reiteration of an 
antecedent in the text, ...) will be applied. These 
preferences will sort the list of remaining 
antecedents, and the first one will be the selected 
antecedent. These constraints and preferences are 
described in more detail in Ferrfindez (1998a), 
Ferrfindez et al. (1998b). 
2.2 Evaluation of the anaphora resolution 
module 
As we reported in Ferr~indez et. al (1998b), we run 
our system on part of the Spanish version of The 
Blue Book corpus. We did not use semantic 
information since the tagger did not provide this 
information, but in spite of this being lacking we 
obtained the following figures: it detected 100% 
of the pronominal anaphors, medium length .of 
sentences with anaphors was 48 words and for 
pronominal anaphora we obtained 83% accuracy 
(pronouns rightly solved divided by the total 
number of pronouns). For The Blue Book in 
English, we have obtained the following figures: 
79 pronouns (it2:41, they:29, themselves:9) with 
an accuracy of 87.3% (it:80,5%, they:93,1%, 
themselves:lO0%); on average, 22 words per 
sentence. 
The reason why some of the references failed is 
mainly due to the lack of semantic information 
and due to some weakness of the English 
grammar that we use. For example, in the 
sentence (1), our system has not selected the right 
antecedent (The French term "communication" 
and the Spanish term "comunicaci6n') due to the 
symbol "(inverted commas) has been tagged as a 
new word, and in our grammar we have not 
foreseen this in a np, so the coordination of both 
np have failed. 
(1) Note 2 - The French term "communication "and the 
Spanish term "comunicaci6n "have the current 
meaning given in this definition, but they also acquire a 
more specific meaning in telecommunication (see 0009, 
0010 and 0011). 
With reference to the differences between English 
and Spanish pronoun resolution, we have 
observed that there is a greater number of possible 
antecedents for Spanish pronouns (26) than for 
English (11). This fact could be due to the larger 
size of Spanish sentences. 
Another difference is that constraints (c-command 
and morphologic agreement) have played a more 
important role for Spanish texts in the detection of 
the antecedent: the total number of possible 
antecedents is reduced from 733 to 222 (a 
reduction of 70%), whereas for English texts it 
has only a reduction of 37.7%. This fact is mainly 
due to the fact that Spanish language has more 
morphologic information than English. 
With regard to the importance of each kind of 
information for each language, if we apply exactly 
the same set of preferences in Spanish and 
English, we obtain a 76% accuracy in English. 
But we have obtained a better accuracy (87.3%) if 
we give" more importance to syntactic parallelism 
and less importance to statistical information. 
3 Interlingual mechanism focused on 
MT: Discrepancy analysis 
In this section, we will present the Interlingual 
mechanism 1SS that takes as input SS' (the final 
2 Pleonastic pronouns it (i.e. non-anaphoric it) have not been 
included in these results. 
71 
slot structure obtained after applying the anaphora 
resolution module) from the source language and 
generates the slot structure in the target language. 
In our proposal, we will study pronominal 
anaphora generation exclusively. We will divide 
the section in several subsections that solve the 
different discrepancies between English and 
Spanish. In Figure 3 we can see the 1SS. 
/ SS' in source language I 
INTERLINGUAL ISS p, 
" 
SS' in target language 
Figure 3. 
3.1 Number discrepancy resolution 
One problem is generated by the discrepancy 
between words of different languages that express 
the same concept. These words can be referred to 
a singular pronoun in the source language and to a 
plural pronoun in the target language. 
We construct a table with the words that refer to a 
singular pronoun in the source language and they 
refer to a plural pronoun in the target language in 
order to be able to solve these discrepancies 
correctly. Firstly, we consult this table in the 
anaphora translation. If the pronoun and its 
antecedent appear in this figure, we will carry out 
the indicated transformation. 
Anteced Span. Anaphor i Eng. Anaphor Anteced 
Policla l~sta ~ ~ ~ They Police 
CJente l~sta ~ i i ~ They People 
l~ste ~ i ~ They Public 
l~sta ~ : ~ They Youth 
l~ste ~ I ~ They Cattle 
l~sta ~ i ~ They Folk 
P6blico 
Juventud 
Ganado 
Crente 
Figure 4. 
In Figure 4, some examples of these words are 
shown. 
In Figure 5 the English-Spanish translation of a 
sentence with number discrepancies is described. 
In this figure, the translation of English SS 3 into 
Spanish SS is shown 
I Thestadiumwasfullofpeoplei. They, wereveryangrY. I 
+ 
List of antecedents: np(conc(singular),X, 
"the stadium"), np(conc(plural),Y, 
"people") 
Anaphor: pron(conc(plural),Z, "they") 
+ 
Final Slot Structure of the Anaphor: pron(conc(plural), np(conc(plural),Y, "~") 
+ 
Transintion of the SS of the Anaphor into Spanish pron(conc(singular), np(conc(feminine,singular),X, 
"'genre"), "~sta") 
El estadio estaba lleno de gentei. I~stai estaba muy disgustada. 
Figure 5. 
This SS stores for each constituent the following 
information: constituent name, semantic and 
morphologic information (structure with functor 
conc), discourse marker (identifier of the entity or 
discourse object) and the SS of its subconstituents. 
As can be observed in Figure 5 we store in the SS 
of pronouns the information of the right 
antecedent obtained after applying the anaphora 
resolution module. 
It is necessary to emphasise that after carrying out 
the translation, the anaphor must agree in number 
and person with the verb of the sentence where it 
appears. 
3.2 Gender discrepancy resolution 
In order to solve personal pronoun gender 
discrepancies, we construct a table that translates 
Spanish personal pronouns into the English ones 
and vice versa. 
In the Spanish-English translation we only have 
problems with the pronoun it. The Spanish 
pronoun 61/6ste (masculine singular third person) 
can be translated into he or it. If the antecedent of 
the pronoun dl/dste refers to a person, we will 
translate it into he. If the antecedent of the 
3 Henceforth, we will write the simplified SS where it solely 
appears the relevant information for each example. 
72 
La gata~ bebia leche. Ella i estaba muy hambrienta. 
List of antecedents: 
np(conc(fem,sing,nonhuman),X, " la gata"), 
np(cone( fem,sing,drinkable),Y, "leche") 
Anaphor: 
prun(cone( fern,sing,third,animal ),Z, "ella") 
+ 
Final Slot Structure of the Anaphor: 
prun(conc(fern,sing,third,animal), 
np(conc( fem,sing,nonhuman),X, "la gata"), "ella") 
Tl~mslation of the SS of the Anaphor into English 
pron(conc(_,sing,third,animal), 
np(conc(_,sing,nonhuman),X, "the cat"), "it") 
÷ 
The cat i was drinking milk. lti was very hungry. 
Figure 6. 
pronoun is an animal or a thing we will translate it 
into it. These characteristics of the antecedent can 
be obtained from the semantic information that it 
is stored in its SS. This semantic information can 
be incorporated to the system using IRSAS 
method Moreno eta/. (1992) or another linguistic 
resource, like WordNet. A similar trouble occurs 
with the Spanish pronoun ellaJdsta which is 
solved in the same way. 
In the example of Figure 6 the third argument of 
the conc structures of these SS is the semantic 
type, according to the IRSAS ontology. As it can 
be observed, the np "the cat" has the semantic 
type nonhuman(animal) and for this reason the 
pronoun ella is translated into the English 
pronoun it. 
f 
SPANISH 
Yo (~rst person singular) 
Td (second person singular) "t 
Usted (second person singular) 
Vosotros (masculine second person plural) 
Vosotras (feminine second person plural) 
Ustedes (second person plural) 
El (masculine third pei'son singular) 
Ella (feminine third person singular) 
Este (masculine third person singular) 
\[;sta (feminine thirdperson singular) 
Nosotros (masculine first person plural) 
Nosotras (feminine first person plural) } 
Ellos (masculine thirdpersonplural) "1 
Elias (feminine third person plural) y 
I 
I 
I 
I 
I 
I 
I 
I 
I 
Figure . 
ENGLISH 
~-- ! ~rst person singular) 
You (secondperson sing. andpl.) 
He (masculine third person singular) "1 
It (third person singular) 
She (feminine third person singular) "~ 
It (third person singular) 
He (masculine third person singular) "1 
It (third person singular) 
She (feminine third person singular) "1 
It (third person singular) 
We (first person plural) 
• " They (thirdpersonplural) 
The table of Figure 7 is used for the remaining 
pronouns and a direct conversion into English is 
made. 
We have analysed that Spanish has more 
morphologic information than English, which is 
extremely relevant in the English-Spanish 
translation. In order to solve this problem and to 
choose the right Spanish pronoun we must obtain 
the gender and number information from the 
antecedent of the anaphora and carry out the 
translation. The pronoun it involves a series of 
problems since it can be translated into four 
different Spanish pronouns (dl, ella, dste, dsta). 
These Spanish pronouns refer to both animals and 
things, but normally dl/ella refers to animals and 
dste/dsta refers to things. Therefore, in our 
automatic Interlingual mechanism, when the 
antecedent of the pronoun is an animal it is 
translated into dl/ella and when it is a thing it is 
translated into dste/dsta, since it is the most 
common use in Spanish. 
Finally, an additional difficulty exists in the 
translation of the pronoun you. In Spanish, there 
are two pronouns for the singular second person 
(tzi or usted) and three pronouns for the plural 
second person (vosotros/vosotras or ustedes). 
Basically, the difference lies on which the 
pronouns tfdvosotros/vosotras are used in an 
73 
informal language (colloquial) whereas 
usted/ustedes are used in a formal one. This 
implies that to have a specific knowledge of the 
situation is necessary to be able to choose the 
right pronoun. Our proposal does not carry out 
word sense disambiguation and, simply, the 
colloquial pronouns t~/vosotros/vosotras will be 
chosen in these cases. 
3.3 Syntactic discrepancy resolution 
This discrepancy is due to the fact that the surface 
structures of the Spanish sentences are more 
flexible than the English ones. The constituents of 
the Spanish sentences can appear in any position 
of the sentence. In orde? to carry out a correct 
translation into English, we must firstly reorganise 
the Spanish sentence. Nevertheless, in the 
English-Spanish translation, in general, this 
reorganisation is not necessary and a direct 
translation can be carried out.   
~ (literally: To Peter him saw yesterday) 
I saw Peter yesterday 
• f~ of the initial sentence in Spanish: 
sentencePP(pp(prep(A), np(eedro)), pron(Io), verb(vO, 
freeWord(ayer)) 
SS of the verb: 
verb(cone( sing,firstPerson ,pas0,Z , "m ~') 
÷ 
Final Slot Structure of the lniti~ sentence in Spanish: 
sentencePP(pron(yo), verb(w'), pp(prep(A), np(Pedro)), 
freeWnrd(ayer)) 
Translation of the sentence into English: 
sentencePP(pron(/), verb(saw), np(Peter), 
freeWord(yesterday)) 
Figure 8. 
Let us see an example with the Spanish sentence 
"A Pedro 1o vi ayer" (1 saw Peter yesterday). In 
this sentence, the object of the verb appears before 
the verb (in the position of the theoretically 
subject) and the subject is omitted. Moreover, 
there is a pronoun, 1o (him) that functions as 
complement of the verb vi (saw). This pronoun in 
Spanish refers to the object of the verb, Pedro 
(Peter), when it is moved from its theoretical 
place after the verb (as it occurs in this sentence). 
In this sentence, the pronominal subject has been 
omitted. We can find out the subject since the 
verb is in first person and singular (information 
stored into its conc structure), so the subject 
would be the pronoun yo (1). Therefore, the 
solution would be a new SS in which the order of 
the constituents is the usual in English: subject, 
verb, complements of the verb. 
In Figure 8, we can see this process graphically. 
In this sentence, the pp ("a Pedro ") functions as a 
indirect object of the verb (because it has the 
preposition a (to)), and the subject of the verb has 
to be in first person and singular. After 
reorganising the sentence, we carry out the 
translation of each constituent. The words that 
have not been parsed (freeWord) are translated 
into the appropriate words in the target language. 
3.4 Elliptical zero-subject construction 
resolution 
Omitting the pronominal subject is usual in 
Spanish. In these cases, we get the number and 
person information from the verb to obtain the 
corresponding English pronoun. 
Pedro gan6 el partido de tenis. ~ $61o perdi6 un set. \] 
÷ 
SS of the sentence 
sentence(np("Pedro"), vp("gon6 el pa ido de tenis") 
sentence( , vp("Sdlo perdi6 un set") 
verb( eonc(past~thirdPerson,sing,animal,inorganic ) ,X,"perdi6") 
Omitted Anaphor: 
pron( conc(_,sing,third,animal),Z, "~l/ella") 
÷ 
Final Slot Structure of the Anaphor: \] i 
pron( conc(m ase,sing,third,anim al), I 
np( con e(masc~sin f~humen),X, "Peter"), "~1") 
÷ 
Translation of the SS of the Anaphor into English \] 
i 
pron( cone( mase,sing,third,anim al), I 
np(conc(mase,sing,human),X, "Peter"), "he") 
Peter won the tennis match. He only lost one set. I 1 I 
Figure 9. 
We can check the omission of the pronominal 
subject of a sentence by means of the SS of the 
sentence as it is shown in Figure 9. In this figure, 
we know that the subject of the sentence has been 
omitted due to the Prolog variable that we find. 
When it is omitted in the sentence, the SS would 
have a Prolog variable in the slot corresponding to 
this noun phrase. We can obtain the information 
corresponding to the subject from the verb of the 
sentence. In this figure, it would be third person, 
74 
singular and masculine or feminine. With these 
omitted pronominal anaphors, we will apply the 
preference for the subject of the previous sentence 
(if it agrees in person and number, and if it is 
semantically consistent). This information is used 
to find its antecedent, in this case Pedro (Peter) 
with masculine gender, so the final translation 
would choose a masculine pronoun (he). 
Sometimes, we can also obtain the gender 
information of the pronoun when the verb is 
copulative. For example, in4: Pedroi vio a Anaj en 
el parque, fDj Estaba muy guapa (Peteri saw Ann~ 
in the park. Shej was very beautiful). In this 
example, the verb estaba (was) is copulative, so 
its subject has to agree in gender and number with 
its object. In this way, we can obtain the gender 
information from the object, guapa (beautiful 
woman), that has feminine gender, so the omitted 
pronoun would be she instead of he. 
4 Commercial MT system evaluation 
and discussion 
In this section, we evaluate different commercial 
MT systems analysing their deficiencies in 
translating pronominal anaphora. We study how 
MT systems deal with the presented 
discrepancies. In this paper we evaluate 4 
systems: (1) Key Translator Pro Version 2.0 
(Softkey International), (2) Power Translator 
Professional (Globalink, Inc.), (3) SYSTRAN 
Translation Software (http://babelfish.altavista. 
com/cgi-bin/translate) and (4) DosAmigos version 
4.0 (Worldwide Sales Corp.). 
In Figure 10, it can be observed the translation of 
an English-Spanish sentence with gender 
discrepancies. In (1) and (2) the pronoun they is 
wrongly translated into ellos (masculine plural); 
in (3) and (4) the pronoun is omitted. The 
pronominal subject can be omitted inSpanish. 
However, pronominal anaphora is always 
presented in Spanish in our automatic 1SS 
mechanism. 
The correct translation of this anaphoric 
expression in our system is the pronoun elias 
(feminine plural). The information related to the 
gender and number must be extracted from the 
correct antecedent. 
Source language : Women were in the duty-free shop. They were 
buying gifts for their husbands. 
(1) Mujeres sido en el exento de derechos de aduana tienda. 
Ellos estaban regalos comprantes para sus esposos. 
(2) Las mujeres estaban en la tienda libre de impuestos. Eilos 
compraban los regalos para sus esposos. 
(3) Las mujeres estaban en el departamento con franquicia. 
0 Compraban regalos para sus maridos. 
(4) Las mujeres estuvieron en ia tienda de libre-de-impuestos. 
0 Estuvieron comprando regalos para sus maridos. 
Target language: Las mujeres estaban en la tienda iibre de 
impuestos. Elias estaban comprando regalos para sus maridos. 
Figure lO. 
In figure 11, an English-Spanish translation with 
gender discrepancies can be observed. The 
Spanish pronoun dl is translated into he in (1) (2) 
(3) and (4) while the right translation is the 
pronoun it. In our proposal, we solve the problem 
using semantic information of the antecedent. In 
this case, the antecedent el mono (the monkey) is 
an animal, therefore, the pronoun he must be 
translated into it. 
In figure 12, a number discrepancy can be 
observed. The word police is plural in English, 
while it is singular in Spanish (policia). In (1) (2) 
(3) and (4) we can observed wrong translations 
and pronouns that do not agree with the verb. 
Before the translation, the number discrepancy 
table is consulted and if the pronoun and its 
antecedent appear in this table, we will carry out 
the indicated transformation. After the translation, 
the anaphor must agree in number and person 
with the verb of the sentence where it appears. 
Source language : El mono se bebi6 la leche. Despu6s, dl salt6 
entre los 6rboles. 
(1) The monkey was dmnk the milk. Afterwards, he jumped 
between the trees. 
(2) The monkey was drunk the milk. After, he jumped between 
the trees. 
(3) The monkey drank milk. Later, he jumped between the 
trees. 
(4) The monkey \[bebi6\] milk her/you/it \[Despu6s\], \[61\] \[salt6\] 
1~he~she/you enter the \[~boles\]. 
Target language: The monkey drank milk. Later, it jumped 
between the trees. 
Figure 11. 
4 The symbol ~ in a position of the sentence marks the 
omitted words in that position. 
75 
Source language: The police are coming. They are just in time. 
(1) La policta viene. Ellos son solamente en tiempo. 
(2) Los policlas vienen. Ellos son simplemente en el tiempo. 
(3) El policia est~ viniendo. L~I es justa en tiempo. 
(4) La policia est~ viniendo. 0 Justamente son a tiempo. 
Target language : La polieia es~ viniendo, l~sta ilegar~ a tiempo. 
Figure 12. 
In Figure 13, an example of Spanish-English 
syntactic discrepancies can be observed. The 
systems (1) (2) (3) and (4) fail in the translation. 
In our mechanism, we reorganise the sentence and 
then, we accomplish the translation. 
Source language : A Pedro 1o vi ayer. 
(1) To I Ask for was seen it yesterday. 
(2) To Pedro I saw it yesterday. 
(3) To Pedro I saw yesterday. 
(4) TO/AT Pedro saw him/you/it yesterday. 
Target language : I saw Peter yesterday. 
Figure 13. 
Finally, we analyse the Spanish elliptical 
zero-subject construction. In Figure 14, the 
systems (1) (2) and (4) fail in the translation. In 
our proposal, we obtain the information 
corresponding to the subject from the verb of the 
sentence. In this example, the pronoun must be 
first or third person and singular. We extract the 
gender information from the correct antecedent 
(feminine) and we determine that the pronoun is 
she (ella), feminine third person singular. 
Source language : La mujer tenia hambre. ~1Comia el mel6n. 
(1) The woman was hungry. O Was catting the melon. 
(2) The woman were hungry. O Was eating the melon. 
(3) The woman was hungry. She ate the melon. 
(4) The woman was being hungry. 1~he./she/you was eating the 
melon. 
Target language : The woman was hungry. She ate the melon. 
Figure 14. 
Conclusion 
After the evaluation, we consider that most of the 
MT systems do not deal with anaphora resolution 
and their successful operation usually does not go 
beyond the sentence level. We propose an 
Interlingual mechanism that relate pronouns in 
different languages (English-Spanish) with the 
information stored of the resolution of its 
antecedent allowing us a correct translation 
between both languages. 
The evaluation of the pronoun translation has 
been analysed by hand, where we have obtained 
that if the pronoun resolution is correct, its 
translation as well. However, we have obtained in 
pronominal anaphora resolution: 83% and 87.3% 
accuracy for Spanish and English respectively. 

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