TREQ-AL: A word alignment system with limited language resources 
 
Dan Tufiş, Ana-Maria Barbu, Radu Ion 
Romanian Academy Institute for Artificial Intelligence 
13, “13 Septembrie”, 74311, Bucharest 5, Romania 
{tufis,abarbu,radu}@racai.ro 
 
 
 
Abstract
 
We provide a rather informal presentation of a 
prototype system for word alignment based on 
our previous translation equivalence approach, 
discuss the problems encountered in the 
shared-task on word-aligning of a parallel 
Romanian-English text, present the preliminary 
evaluation results and suggest further ways of 
improving the alignment accuracy. 
 
1 Introduction 
In (Tufiş and Barbu, 2002; Tufiş, 2002) we largely 
described our extractor of translation equivalents, called 
TREQ. It was aimed at building translation dictionaries 
from parallel corpora. We described in (Ide et al. 2002) 
how this program is used in word clustering and in 
checking out the validity of the cross-lingual links 
between the monolingual wordnets of the multilingual 
Balkanet lexical ontology (Stamatou et al. 2002). In this 
paper we describe the TREQ-AL system, which builds 
on TREQ and aims at generating a word-alignment map 
for a parallel text (a bitext). TREQ-AL was built in less 
than two weeks for the Shared Task proposed by the 
organizers of the workshop on “Building and Using 
Parallel Texts:Data Driven Machine Translation and 
Beyond” at the HLT-NAACL 2003
1
 conference. It can 
be improved in several ways that became conspicuous 
when we analyzed the evaluation results. TREQ-AL has 
no need for an a priori bilingual dictionary, as this will 
be automatically extracted by TREQ. However, if such 
a dictionary is available, both TREQ and TREQ-AL 
know to make best use of it. This ability allows both 
systems to work in a bootstrapping mode and to produce 
larger dictionaries and better alignments as they are 
used. 
The word alignment, as it was defined in the shared 
task is different and harder than the problem of 
translation equivalence as previously addressed. In a 
dictionary extraction task one translation pair is 
considered correct, if there is at least one context in 
which it has been rightly observed. A multiply 
occurring pair would count only once for the final 
                                                 
1
 http://www.cs.unt.edu/~rada/wpt/index.html#shared 
dictionary. This is in sharp contrast with the alignment 
task where each occurrence of the same pair equally 
counts. 
Another differentiating feature between the two 
tasks is the status of functional word links. In extracting 
translation equivalents one is usually interested only in 
the major categories (open classes). In our case (because 
of the WordNet centered approach of our current 
projects) we were especially interested in POS-
preserving translation equivalents. However, since in 
EuroWordNet and Balkanet one can define cross-POS 
links, the different POS translation equivalents became 
of interest (provided these categories are major ones).  
The word alignment task requires each word 
(irrespective of its POS) or punctuation mark in both 
parts of the bitext be assigned a translation in the other 
part (or the null translation if the case).  
Finally, the evaluations of the two tasks, even if 
both use the same measures as precision or recall, have 
to be differently judged. The null alignments in a 
dictionary extraction task have no significance, while in 
a word alignment task they play an important role (in 
the Romanian-English gold standard data the null 
alignments represent 13,35% of the total number of 
links).  
 
2 The preliminary data processing  
The TREQ system requires sentence aligned parallel 
text, tokenized, tagged and lemmatized. The first 
problem we had with the training and test data was 
related to the tokenization. In the training data there 
were several occurrences of glued words (probably due 
to a problem in text export of the initial data files) plus 
an unprintable character (hexadecimal code A0) that 
generated several tagging errors due to guesser 
imperfect performance (about 70% accurate). 
To remedy these inconveniences we wrote a script 
that automatically split the glued words and eliminated 
the unprintable characters occurring in the training data. 
The set of splitting rules, learnt from the training 
data was posted on the site of the shared task. The set of 
rules is likely to be incomplete (some glued words 
might have survived in the training data) and also might 
produce wrong splitting in some cases (e.g. turnover 
being split always in turn over).  
The text tokenization, as considered by the 
evaluation protocol, was the simplest possible one, with 
white spaces and punctuation marks taken as separators. 
The hyphen (‘-‘) was always considered a separator and 
consequently taken to be always a token by itself. 
However, in Romanian, the hyphen is more frequently 
used as an elision marker (as in “intr-o”= “intru o”/in a), 
a clitics separator (as in “da-mi-l”=”da –mi –l”=”da mie 
el”/give to me it/him) or as a compound marker (as in 
“terchea-berchea” /(approx.) loafer) than as a separator.  
In such cases the hyphen cannot be considered a token. 
A similar problem appeared in English with respect to 
the special quote character, which was dealt with in 
three different ways: it was sometimes split as a distinct 
token (we’ll = we + ’ + ll), sometimes was adjoined to 
the string (a contracted positive form or a genitival) 
immediately following it (I’m = I + ’m, you’ve =  
you+’ve,  man’s = man + ’s etc.) and systematically left 
untouched in the negative contracted forms (couldn’t, 
wasn’t, etc).   
Since our processing tools (especially the tokeniser) 
were built with a different segmentation strategy in 
mind, we generated the alignments based on our own 
tokenization and, at the end, we “re-tokenised” the text 
according to the test data model (and consequently re-
index) all the linking pairs.  
For tagging the Romanian side of the training bitext 
we used the tiered-tagging approach (Tufiş, 1999) but 
we had to construct a new language model since our 
standard model was created from texts containing 
diacritics. As the Romanian training data did not contain 
diacritical characters, this was by no means a trivial task 
in the short period of time at our disposal (actually it 
took most of the training time). The lack of diacritics in 
the training data and the test data induced spurious 
ambiguities that degraded the tagging accuracy with at 
least 1%. This is to say that we estimate that on a 
normal Romanian text (containing the diacritical 
characters) the performance of our system would have 
been better. The English training data was tagged by 
Eric Gaussier, warmly acknowledged here. As the 
tagsets used for the two languages in the parallel 
training corpus were quite different, we defined a tagset 
mapping and translated the tagging of the English part 
into a tagging closer to the Romanian one. This 
mapping introduced some ambiguities that were solved 
by hand. Based on the training data (both Romanian and 
English texts), tagged with similar tagsets, we built the 
language models used for the test data alignment. 
POS-preserving translation equivalence is a too 
restrictive condition for the present task and we defined 
a meta-tagset, common for both languages that 
considered frequent POS alternations. For instance, the 
verb, noun and adjective tags, in both languages were 
prefixed with a common symbol, given that verb-
adjective, noun-verb, noun-adjective and the other 
combinations are typical for Romanian-English 
translation equivalents that do not preserve the POS. 
With these prefixes, the initial algorithm for extracting 
POS-preserving translation equivalents could be used 
without any further modifications. Using the tag-
prefixes seems to be a good idea not only for legitimate 
POS-alternating translations, but also for overcoming 
some typical tagging errors, such as participles versus 
adjectives. In both languages, this is by far the most 
frequent tagging error made by our tagger. 
The last preprocessing phase is encoding the corpus 
in a XCES-Align-ana format as used in the MULTEXT-
EAST corpus (see http://nl.ijs.si/ME/V2/) which is the 
standard input for the TREQ translation equivalents 
extraction program. Since the description of TREQ is 
extensively given elsewhere, we will not go into further 
details, except of saying that the resulted translation 
dictionary extracted from the training data contains 
49283 entries (lemma-form). The filtering of the 
translation equivalents candidates (Tufiş and Barbu, 
2002) was based on the log-likelihood and the cognate 
scores with a threshold value set to 15 and 0,43 
respectively.  We roughly estimated the accuracy of this 
dictionary based on the aligned gold standard: precision 
is about 85% and recall is about 78% (remember, the 
dictionary is evaluated in terms of lemma entries, and 
the non-matching meta-category links are excluded).  
 
3 The TREQ-AL linking program  
This program takes as input the dictionary created by 
TREQ and the parallel text to be word-aligned. The 
alignment procedure is a greedy one and considers the 
aligned translation units independent of the other 
translation units in the parallel corpus.  It has 4 steps: 
1. left-to-right pre-alignment 
2. right-to-left adjustment of the pre-alignment 
3. determining alignment zones and filtering them out 
4. the word-alignment inside the  alignment zones 
 
3.1 The left-to-right pre-alignment 
For each sentence-alignment unit, this step scans the 
words from the first to the last in the source-language 
part (Romanian). The considered word is initially linked 
to all the words in the target-language part (English) of 
the current sentence-alignment unit, which are found in 
the translation dictionary as potential translations. If for 
the source word no translations are identified in the 
target part of the translation unit, the control advances to 
the next source word. The cognate score and the relative 
distance are decision criteria to choose among the 
possible links. When consecutive words in the source 
part are associated with consecutive or close to each 
other words in the target part, these are taken as forming 
an “alignment chain” and, out of the possible links, are 
considered those that correspond to the densest 
grouping of words in each language. High cognate 
scores in an alignment chain reinforce the alignment. 
One should note that at the end of this step it is possible 
to have 1-to-many association links if multiple 
translations of one or more source words are found in 
the target part of the current translation unit (and, 
obviously, they satisfy the selection criteria). 
 
3.2 The right-to-left adjustment of the pre-alignment 
This step tries to correct the pre-alignment errors (when 
possible) and makes a 1-1 choice in case of the 1-m 
links generated before. The alignment chains (found in 
the previous step) are given the highest priority in 
alignment disambiguation. That is, if for one word in 
the source language there are several alignment 
possibilities, the one that belongs to an alignment chain 
is always selected. Then, if among the competing 
alignments one has a cognate score higher than the 
others then this is the preferred one (this heuristics is 
particularly useful in case of several proper names 
occurring in the same translation unit). Finally, the 
relative position of words in the competing links is 
taken into account to minimize the distance between the 
surrounding already aligned words. 
The first two phases result in a 1-1 word mapping. 
The next two steps use general linguistic knowledge 
trying to align the words that remain unaligned (either 
due to no translation equivalents or because of failure to 
meet the alignment criteria) after the previous steps. 
This could result in n-m word alignments, but also in 
unlinking two previously linked words since a wrong 
translation pair existing in the extracted dictionary 
might license a wrong link.  
 
3.3 Alignment zones and filtering suspicious links out 
An alignment zone (in our approach) is a piece of text 
that begins with a conjunction, a preposition, or a 
punctuation mark and ends with the token preceding the 
next conjunction, preposition, punctuation or end of 
sentence. A source-language alignment zone is mapped 
to one or more target-language alignment zones via the 
links assigned in the previous steps (based on the 
translation equivalents). One has to note that the 
mapping of the alignment zones is not symmetric. An 
alignment zone that contains no link is called a virgin 
zone. 
In most of the cases the words in the source 
alignment zone (starting zone) are linked to words in the 
target alignment zone/s (ending zone/s). The links with 
either side outside the alignment zones are suspicious 
and they are deleted. This filtering proved to be almost 
100% correct in case the outlier resides in a zone non-
adjacent to the starting or ending zones. The failures of 
this filtering were in the majority of cases due to a 
wrong use of punctuation in one or the other part of the 
translation unit (such as omitted comma, a comma 
between the subject and predicate). 
 
3.4 The word-alignment inside the alignment zones 
For each un-linked word in the starting zone the 
algorithm looks for a word in the ending zone/s of the 
same category (not meta-category). If such a mapping 
was not possible, the algorithm tries to link the source 
word to a target word of the same meta-category, thus 
resulting in a cross-POS alignment. The possible meta-
category mappings are specified by the user in an 
external mapping file. Any word in the source or target 
languages that is not assigned a link after the four 
processing steps described above is automatically 
assigned a null link. 
 
4 Post-processing  
As said in the second section, our tokenization was 
different from the tokenization in the training and test 
data. To comply with the evaluation protocol, we had to 
re-tokenize the aligned text and re-compute the indexes 
of the links. Re-tokenizing the text meant splitting 
compounds and contracted future forms and gluing 
together the previously split negative contracted forms 
(do+n’t=don’t). Although the re-tokenization was a 
post-processing phase, transparent for the task itself, it 
was a source of missing some links for the negative 
contracted forms. In our linking the English “n’t” was 
always linked to the Romanian negation and the English 
auxiliary/modal plus the main verb were linked to the 
Romanian translation equivalent found for the main 
verb. Some multi-word expressions recognized by the 
tokenizer as one token, such as dates (25 Ianuarie, 
2001), compound prepositions (de la, pina la), 
conjunctions (pentru ca, de cind, pina cind) or adverbs 
(de jur imprejur, in fata) as well as the hyphen 
separated nominal compounds (mass-media, prim-
ministru) were split, their positions were re-indexed and 
the initial one link of a split compound was replaced 
with the set obtained by adding one link for each 
constituent of the compound to the target English word. 
If the English word was also a compound the number of 
links generated for one aligned multiword expression 
was equal to the N*M, where N represented the number 
of words in the source compound and M the number of 
words in the target compound.   
5 Evaluation 
The results of the evaluation of TREQ-AL performance 
are shown in the Table 1. In our submission file the 
sentence no. 221 was left out by (our) mistake. We used 
the official evaluation program to re-evaluate our 
submission with the omitted sentence included and the 
precision improved with 0,09%, recall with 0,45%, F-
measure and AER with 0,33%.). The figures in the first 
and second columns of the Table 1 are those considered 
by the official evaluation. The last column contains the 
evaluation of the result that was our main target. Since 
TREQ-AL produces only “sure” links, AER (alignment 
error rate - see the Shared Task web-page for further 
details) reduces to 1 - F-measure. 
TREQ-AL uses no external bilingual-resources. A 
machine-readable bilingual dictionary would certainly 
improve the overall performance.  The present version 
of the system (which is far from being finalized) seems 
to work pretty well on the non-null assignments and this 
is not surprising, because these links are supposed to be 
relevant for a translation dictionary extraction system 
and this was the very reason we developed TREQ.  
Moreover if we consider only the content words (main 
categories: noun, verbs, adjectives and general adverbs), 
which are the most relevant with respect to our 
immediate goals (multilingual wordnets interlinking and 
word sense disambiguation), we think TREQ-AL 
performs reasonably well and is worth further 
improving it. 
 
 Non-null 
links only 
Null links 
included 
Dictionary 
entries 
Precision 81,38% 60,43% 84,42% 
Recall 60,71% 62,80% 77,72% 
F-measure 69,54% 61,59% 80,93% 
AER 30,46% 38,41% 
 Table 1. Evaluation results 
6 Conclusions and further work 
TREQ-AL was developed in a short period of time and 
is not completely tested and debugged. At the time of 
writing we already noticed two errors that were 
responsible for several wrong or missed links. There are 
also some conceptual limitations which, when removed, 
are likely to further improve the performance. For 
instance all the words in virgin alignment zones are 
automatically given null links but the algorithm could 
be modified to assign all the links in the Cartesian 
product of the words in the corresponding virgin zones. 
The typical example for such a case is represented by 
the idiomatic expressions (tanda pe manda = the list 
that sum up). A bilingual dictionary of idioms as an 
external resource certainly would significantly improve 
the results. Also, with an additional preprocessing 
phase, for collocation recognition, many missing links 
could be recovered. At present only those collocations 
that represent 1-2 or 2-1 alignments are recovered. 
A major improvement will be to make the 
algorithm symmetric. There are many cases when 
reversing the source and target languages new links can 
be established. This can be explained by different 
polysemy degrees of the translation equivalent words 
and the way we associate alignment zones. 
The word order in Romanian and English to some 
extent is similar, but in the present version of TREQ-AL 
this is not explicitly used. One obvious and easy 
improvement of TREQ-AL performance would be to 
take advantage of the similarity in word order and map 
the virgin zones and afterwards, the words in the virgin 
zones. 
Finally, we noticed in the gold standard some 
wrong alignments. One example is the following:  
“… a XI – a …” = “… eleventh…” 
Our program aligned all the 4 tokens in Romanian (a, 
XI, –, a) to the English token (eleventh), while the gold 
standard assigned only “XI” to “eleventh” and the other 
three Romanian tokens were given a null link.  We also 
noticed some very hard to achieve alignments 
(anaphoric links).   
 
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