Proceedings of the Workshop on Statistical Machine Translation, pages 162–165,
New York City, June 2006. c©2006 Association for Computational Linguistics
N-gram-based SMT System Enhanced with Reordering Patterns
Josep M. Crego
Marta R. Costa-juss a
Jos·e B. Mari no
Adri a de Gispert
Maxim Khalilov
Jos·e A. R. Fonollosa
Department of Signal Theory and Communications
TALP Research Center (UPC)
Barcelona 08034, Spain
{jmcrego,agispert,lambert,mruiz,khalilov,rbanchs,canton,adrian}@gps.tsc.upc.edu
Patrik Lambert
Rafael E. Banchs
Abstract
This work presents translation results for
the three data sets made available in the
shared task  Exploiting Parallel Texts for
Statistical Machine Translation of the
HLT-NAACL 2006 Workshop on Statisti-
cal Machine Translation. All results pre-
sented were generated by using the N-
gram-based statistical machine translation
system which has been enhanced from the
last year’s evaluation with a tagged target
language model (using Part-Of-Speech
tags). For both Spanish-English transla-
tion directions and the English-to-French
translation task, the baseline system al-
lows for linguistically motivated source-
side reorderings.
1 Introduction
The statistical machine translation approach used
in this work implements a log-linear combination
of feature functions along with a translation model
which is based on bilingual n-grams (de Gispert and
Mari no, 2002).
This translation model differs from the well
known phrase-based translation approach (Koehn
et al., 2003) in two basic issues:  rst, training data
is monotonously segmented into bilingual units; and
second, the model considers n-gram probabilities in-
stead of relative frequencies. This translation ap-
proach is described in detail in (Mari no et al., 2005).
For those translation tasks with Spanish or En-
glish as target language, an additional tagged (us-
ing POS information) target language model is used.
Additionally a reordering strategy that includes POS
information is described and evaluated.
Translation results for all six translation directions
proposed in the shared task are presented and dis-
cussed. Both translation directions are considered
for the pairs: English-Spanish, English-French,
and English-German.
The paper is structured as follows: Section 2
brie y outlines the baseline system. Section 3 de-
scribes in detail the implemented POS-based re-
ordering strategy. Section 4 presents and discusses
the shared task results and,  nally, section 5 presents
some conclusions and further work.
2 Baseline N-gram-based SMT System
As already mentioned, the translation model used
here is based on bilingual n-grams. It actually con-
stitutes a language model of bilingual units, referred
to as tuples, which approximates the joint probabil-
ity between source and target languages by using
bilingual n-grams (de Gispert and Mari no, 2002).
Tuples are extracted from a word-to-word aligned
corpus according to the following two constraints:
 rst, tuple extraction should produce a monotonic
segmentation of bilingual sentence pairs; and sec-
ond, no smaller tuples can be extracted without vi-
olating the previous constraint. See (Crego et al.,
2004) for further details.
For all experiments presented here, the translation
model consisted of a 4-gram language model of tu-
ples. In addition to this bilingual n-gram translation
model, the baseline system implements a log linear
combination of  ve feature functions.
162
These  ve additional models are:
 A target language model. 5-gram of the target
side of the bilingual corpus.
 A word bonus. Based on the number of tar-
get words in the partial-translation hypothesis,
to compensate the LM preference for short sen-
tences.
 A Source-to-target lexicon model. Based on
IBM Model 1 lexical parameters(Brown et al.,
1993), providing a complementary probability
for each tuple in the translation table. These
parameters are obtained from source-to-target
alignments.
 A Target-to-source lexicon model. Analo-
gous to the previous feature, but obtained from
target-to-source alignments.
 A Tagged (POS) target language model. This
feature implements a 5-gram language model
of target POS-tags. In this case, each trans-
lation unit carried the information of its target
side POS-tags, though this is not used for trans-
lation model estimation (only in order to eval-
uate the target POS language model at decod-
ing time). Due to the non-availability of POS-
taggers for French and German, it was not pos-
sible to incorporate this feature in all transla-
tion tasks considered, being only used for those
translation tasks with Spanish and English as
target languages.
The search engine for this translation system is
described in (Crego et al., 2005) and implements
a beam-search strategy based on dynamic program-
ming, taking into account all feature functions de-
scribed above, along with the bilingual n-gram trans-
lation model. Monotone search is performed, in-
cluding histogram and threshold pruning and hy-
pothesis recombination.
An optimization tool, which is based on a down-
hill simplex method was developed and used for
computing log-linear weights for each of the feature
functions. This algorithm adjusts the weights so that
a non-linear combination of BLEU and NIST scores
is maximized over the development set for each of
the six translation directions considered.
This baseline system is actually very similar to
the system used for last year’s shared task  Exploit-
ing Parallel Texts for Statistical Machine Transla-
tion of ACL’05 Workshop on Building and Us-
ing Parallel Texts: Data-Driven Machine Translation
and Beyond (Banchs et al., 2005), whose results
are available at: http://www.statmt.org/wpt05/
mt-shared-task/. A more detailed description of
the system can be found in (2005).
The tools used for POS-tagging were Freel-
ing (Carreras et al., 2004) for Spanish and
TnT (Brants, 2000) for English. All language mod-
els were estimated using the SRI language mod-
eling toolkit. Word-to-word alignments were ex-
tracted with GIZA++. Improvements in word-to-
word alignments were achieved through verb group
classi cation as described in (de Gispert, 2005).
3 Reordering Framework
In this section we outline the reordering framework
used for the experiments (Crego and Mari no, 2006).
A highly constrained reordered search is performed
by means of a set of reordering patterns (linguisti-
cally motivated rewrite patterns) which are used to
extend the monotone search graph with additional
arcs.
To extract patterns, we use the word-to-word
alignments (the union of both alignment directions)
and source-side POS tags. The main procedure con-
sists of identifying all crossings produced in the
Figure 1: Reordering patterns are extracted using
word-to-word alignments. The generalization power
is achieved through the POS tags. Three instances of
different patterns are extracted using the sentences
in the example.
163
word-to-word alignments. Once a crossing has been
detected, its source POS tags and alignments are
used to account for a new instance of pattern. The
target side of a pattern (source-side positions after
reordering), is computed using the original order
of the target words to which the source words are
aligned. See  gure 1 for a clarifying example of
pattern extraction.
The monotone search graph is extended with re-
orderings following the patterns found in training.
The procedure identi es  rst the sequences of words
in the input sentence that match any available pat-
tern. Then, each of the matchings implies the ad-
dition of an arc into the search graph (encoding the
reordering learnt in the pattern). However, this ad-
dition of a new arc is not performed if a translation
unit with the same source-side words already exists
in the training. Figure 2 shows an example of the
procedure.
Figure 2: Three additional arcs have been added
to the original monotone graph (bold arcs) given
the reordering patterns found matching any of the
source POS tags sequence.
Once the search graph is built, the decoder tra-
verses the graph looking for the best translation.
Hence, the winner hypothesis is computed using
all the available information (the whole SMT mod-
els). The reordering strategy is additionally sup-
ported by a 5-gram language model of reordered
source POS-tags. In training, POS-tags are re-
ordered according with the extracted reordering pat-
terns and word-to-word links. The resulting se-
quence of source POS-tags are used to train the n-
gram LM.
Notice that this reordering framework has only
been used for some translation tasks (Spanish-
to-English, English-to-Spanish and English-to-
French). The reason is double:  rst, because we
did not have available a French POS-tagger. Second,
because the technique used to learn reorderings (de-
tailed below) does not seem to apply for language
pairs like German-English, because the agglutina-
tive characteristic of German (words are formed by
joining morphemes together).
Table 1: BLEU, NIST and mWER scores (com-
puted using two reference translations) obtained for
both translation directions (Spanish-to-English and
English-to-Spanish).
Conf BLEU NIST mWER
Spanish-to-English
base 55.23 10.69 34.40
+rgraph 55.59 10.70 34.23
+pos 56.39 10.75 33.75
English-to-Spanish
base 48.03 9.84 41.18
+rgraph 48.53 9.81 41.15
+pos 48.91 9.91 40.29
Table 1 shows the improvement of the original
baseline system described in section 2 (base), en-
hanced using reordering graphs (+rgraph) and pro-
vided the tagged-source language model (+pos).
The experiments in table 1 were not carried out over
the of cial corpus of this shared task. The Spanish-
English corpus of the TC-Star 2005 Evaluation was
used. Due to the high similarities between both cor-
pus (this shared task corpus consists of a subset of
the whole corpus used in the TC-Star 2005 Evalua-
tion), it makes sense to think that comparable results
would be obtained.
It is worth mentioning that the of cial corpus of
the shared task (HLT-NAACL 2006) was used when
building and tuning the present shared task system.
4 Shared Task Results
The data provided for this shared task corresponds
to a subset of the of cial transcriptions of the Euro-
pean Parliament Plenary Sessions. The development
set used to tune the system consists of a subset (500
 rst sentences) of the of cial development set made
available for the Shared Task.
164
Table 2 presents the BLEU, NIST and mWER
scores obtained for the development-test data set.
The last column shows whether the target POS lan-
guage model feature was used or not. Computed
scores are case sensitive and compare to one refer-
ence translation. Tasks in bold were conducted al-
lowing for the reordering framework. For French-
to-English task, block reordering strategy was used,
which is described in (Costa-juss a et al., 2006). As it
can be seen, for the English-to-German task we did
not use any of the previous enhancements.
Table 2: Translation results
Task BLEU NIST mWER tPOS
en ! es 29.50 7.32 58.95 yes
es ! en 30.29 7.51 57.72 yes
en ! fr 30.23 7.40 59.76 no
fr ! en 30.21 7.61 56.97 yes
en ! de 17.40 5.61 71.18 no
de ! en 23.78 6.70 65.83 yes
Important differences can be observed between
the German-English and the rest of translation tasks.
They result from the greater differences in word
order present in this language pair (the German-
English results are obtained under monotone decod-
ing conditions). Also because the greater vocabulary
of words of German, which increases sparseness in
any task where German is envolved. As expected,
differences in translation accuracy between Spanish-
English and French-English are smaller.
5 Conclusions and Further Work
As it can be concluded from the presented results,
although in principle some language pairs (Spanish-
English-French) seem to have very little need for re-
orderings (due to their similar word order), the use
of linguistically-based reorderings proves to be use-
ful to improve translation accuracy.
Additional work is to be conducted to allow for
reorderings when translating from/to German.
6 Acknowledgments
This work was partly funded by the European Union
under the integrated project TC-STAR1: Technology
and Corpora for Speech to Speech Translation (IST-
2002-FP6-506738) and the European Social Fund.
1http://www.tc-star.org

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