The NAIST Machine Translation System for IWSLT2012
Graham Neubig, Kevin Duh, Masaya Ogushi, Takamoto Kano
Tetsuo Kiso, Sakriani Sakti, Tomoki Toda, Satoshi Nakamura
Graduate School of Information Science
Nara Institute of Science and Technology, Japan
Abstract
This paper describes the NAIST statistical machine translation system for the IWSLT2012 Evaluation Campaign. We
participated in all TED Talk tasks, for a total of 11 languagepairs. For all tasks, we use the Moses phrase-based decoder
and its experiment management system as a common basefor building translation systems. The focus of our work is on
performing a comprehensive comparison of a multitude ofexisting techniques for the TED task, exploring issues suchas out-of-domain data filtering, minimum Bayes risk decoding, MERT vs. PRO tuning, word alignment combination,and morphology.

Decoding
Baseline
NAIST Submission

dev2010
26.02
27.05

tst2010
29.75
31.81

Table 1: The scores for systems with and without the proposed improvements.

ogy processing and large language models, which resulted
in an average gain of 1.18 BLEU points over all languages.
Section 4 describes these results in further detail.

2. English-French System
1. Introduction
This paper describes the NAIST participation in the IWSLT2012 evaluation campaign [1]. We participated in all 11
TED tasks, dividing our efforts in half between the official
English-French track and the 10 other unofficial ForeignEnglish tracks. For all tracks we used the Moses decoder [2]and its experiment management system to run a large numberof experiments with different settings over many language
pairs.
For the English-French system we experimented with anumber of techniques, settling on a combination that provided significant accuracy improvements without introducing unnecessary complexity into the system. In the end,
we chose a four-pronged approach consisting of using the
web data with filtering to remove noisy sentences, phrase table smoothing, language model interpolation, and minimum
Bayes risk decoding. This led to a score of 31.81 BLEU on
the tst2010 data set, a significant increase over 29.75 BLEU
of a comparable system without these improvements. In Section 2 we describe each of the methods in more detail andexamine their contribution to the accuracy of the system. For
reference purposes, in Section 3, we also present additional
experiments that gave negative results, which were not included in our official submission.
For the 10 translation tasks into English, we focused ontechniques that could be used widely across all languages. In
particular, we experimented with unsupervised approaches
to handling source-side morphology, minimum Bayes risk
decoding, and large language models. In the end, most of
our systems used a combination of unsupervised morphol-

The NAIST English-French translation system for IWSLT2012 was based on phrase-based statistical machine translation [3] using the Moses decoder [2] and its corresponding
training regimen. Overall, we made four enhancements overthe standard Moses setup to improve the translation accuracy:
Large-scale Data with Filtering: In order to use the large,
but noisy parallel training data in the English-French
Giga Corpus, we implemented a technique to filter out
noisy translated text.
Phrase Table Smoothing: We performed phrase tablesmoothing to improve the probability estimates of
low-frequency phrases.
Language Model Interpolation: In order to adapt to thedomain of the task, we interpolated language models
trained using text from several domains.
Minimum Bayes-Risk Decoding: We used lattice-basedminimum Bayes risk decoding to select hypotheses
that are supported by other hypotheses in the n-best
list, and calibrated the probability distribution to further improve performance.
We demonstrate our results (in BLEU score) before andafter these techniques are added in Table 1. It can be seenthat the combination of these 4 improvements leads to a 2.06
point gain in BLEU score on tst2010 over the baseline system. We will explain each of the techniques in detail as follows.

Corpus
TED
News Commentary (NC)EuroParl (EP)United Nations (UN)WMT2012 Giga
Giga (+Filtering)

English
2.36M
2.99M
50.3M
302M
575M
485M

French
2.47M
3.45M
52.5M
338M
672M
565M

Giga Data
None
Unfiltered
Filtereddev2010
26.61
27.03
27.05

tst2010
31.52
31.90
31.81

Table 3: Accuracy given various styles of using the Giga
data.

Table 2: The number of words in each corpus.
and three indicator features J > I, I > J, I = J.
2.1. Data
The first step of building our system was preparing the data.
Table 2 shows the size and genre of each of the corpora available for the task. From these corpora, we used TED, NC,
EuroParl, UN, and Giga for training the language model, andTED, NC, EuroParl, and filtered Giga (explained below) for
training the translation model.1 Tuning was performed on
dev2010, and testing was performed on tst2010.
In particular, the English-French Giga-word corpus isfrom the web and thus covers a wide variety of diverse topics,
making it a strong ally for the construction of a general domain machine translation system. However, as the sentenceswere automatically extracted, they contain a significant number of errors where the content of the parallel sentences actually do not match, or only match partially. In order to filterout some of this noise, we re-implemented a variant of thesentence filtering method of [4].
The method works by using a clean corpus to train aclassifier that can detect mis-aligned sentences. Because
the clean corpus only contains correctly aligned sentences,
we create pseudo-negative examples by traversing the corpus and randomly swapping two consecutive sentences withsome set probability. These swapped sentences are labeled
as “negative,” and the remainder of the unswapped samples
are labeled as positive.
In this application, the feature set chosen for the classifiermust satisfy two desiderata. First, as with all machine learning applications, the features must be sufficient to discriminate between the classes that we are interested in: properly or
improperly aligned sentences. Second, as our training data (aclean corpus) and testing data (a noisy corpus) will necessarily be drawn from different domains, we would like to use asmall, highly generalizable feature set that will work on both
domains. In order to achieve both of these objectives, wetake hints from [4] and [5] to define the following features,
where f1J and eI1 are the source and target sentences, and J
and I are their respective lengths:

Model One Probability features capture the fact that an unsupervised alignment model (in this case, the efficiently calculable IBM Model One [6]) should assign higher probability to well-aligned sentences.
In this category, we use two continuous featureslog PM 1 (eI1 |f1J ) and log PM 1 (f1J |eI1 ).
Alignment features use Viterbi word alignments and capture certain patterns that should occur in properlyaligned sentences. Word alignments are calculatedusing IBM Model One, and symmetrized using the“intersection” criterion [7]. If the number of aligned
words is K, our features include aligned word ratio K/min(I, J), total number of aligned words K,
number of alignments that are monotonic, monotonicalignment ratio, and the average length of gaps between words (similar to “distortion” used in phrasebased MT [3]).
Same Word features count the number of times that a wordof length n is exactly equal to a word in the opposite sentence. This is useful for noticing when proper
names, numbers, or words with a shared linguistic origin occur in both sentences. In our system we use separate features for n = 1, n = 2, n = 3, and n ≥ 4.

Length Ratio features capture the fact that properly aligned
sentences should be approximately the same length.
Two continuous features max(J, I)/min(J, I), J/I,

To train the non-parallel sentence identifier, we use datafrom the TED, NC, and EuroParl corpora swapping sentences with a probability of 0.3 to create pseudo-negative examples. We use this as training data for a support vector machine (SVM) classifier, which we train using LIBLINEAR[8]. In order to get an estimate of the accuracy of sentence
filtering, we perform 8-fold cross validation on the training
data, and achieve a classification accuracy of 98.0%.2
Next, we run the trained classifier on the entirety of
the Giga corpus and remove the examples labeled as nonparallel. As a result of filtering with the classifier, a total of
485M English and 565M French words remained, a total of
84.3% of the original corpus.
Finally, using no Giga data, the unfiltered Giga data, andthe filtered Giga data (in addition to all other data sets), we
measured the final accuracy of the translation system. The

1 We also attempted to use the UN corpus for training the translation
model, but found that it provided no gain, likely because of the specialized
writing style of UN documents.

2 Of course, as we are using pseudo-negative examples in the Europarl
corpus instead of real negative examples from the Giga corpus, these accuracy features are only approximate.

Smoothing
None
Good-Turing

dev2010
26.75
27.05

tst2010
31.19
31.81

Table 4: BLEU results using translation model smoothing.

LMTED Only
Without Interp.
With Interp.

dev2010
24.80
26.30
27.05

tst2010
29.44
31.15
31.81

Table 5: Results training the language model on only TED
data, and when other data is used without and with language
model interpolation.

results are shown in Table 3. As a result, we can see that using the data from the Giga corpus has a positive effect on the
results, but filtering does not have a clear significant effect on
the results.

2.4. Minimum Bayes Risk Decoding
Finally, we experimented with improved decoding strategies
for translation, particularly using minimum Bayes risk decoding (MBR, [12]). In normal translation, the decoder attempts to simply find the answer with the highest probability
among the translation candidatesÊ = argmaxE P (E|F )(1)

in a process called Viterbi decoding. As an alternative to this,MBR attempts to find the hypothesis that minimizes risk∑
Ê = argminE
P (E 0 |F )L(E 0 , E)(2)
E 0 ∈E

considering the posterior probability P (E 0 |F ) of hypotheses
E 0 in the space of all possible hypotheses E, as well as aloss L(E 0 , E) which determines how bad a translation E is
if the true translation is E 0 . In this work (as with most otherson MBR in MT) we use one minus sentence-wise BLEU+1
score [13] as our loss function
L(E 0 , E) = 1 − BLEU+1(E 0 , E).

(3)2.2. Phrase Table Smoothing
We also performed experiments that used smoothing of thestatistics used in calculating translation model probabilities[9]. The motivation behind this method is that the statistics
used to train the phrase table are generally sparse, and tend toover-estimate the probabilities of rare events. In the submitted system we used Good-Turing smoothing for the phrase
table probabilities.
Results comparing a system with smoothing and withoutsmoothing can be found in Figure 4. It can be seen that GoodTuring smoothing of the phrase table improves results by asignificant amount.
2.3. Language Model Interpolation
One of the characteristics of the IWSLT TED task is that, asshown in Table 2, we have several heterogeneous corpora.
In addition, the in-domain TED data is relatively small, so it
can be expected that we will benefit from using data outsideof the TED domain. In order to effectively utilize out-ofdomain data in language modeling, we build one language
model for each domain and interpolate the language models
to minimize perplexity on the TED dev2010 set using themethod described by [10] and implemented in the SRILM
toolkit [11].
To measure the effectiveness of this technique, we alsomeasure the accuracy without any data other than TED, andwhen the data from all domains was simply concatenated together for LM learning. The results can be found in Table
5. We can see that adding the larger non-TED data to thelanguage model is essential, and using linear interpolation
to adjust the language model weights can also provide large
further gains.

In initial research on MBR, the space of possible hypotheses E was defined as the n-best list output by the decoder. This was further expanded by [14], who defined MBR
over lattices. We tested both of these approaches (as implemented in the Moses decoder).
Finally, one fine point about MBR is that it requires agood estimate of the probability P (E 0 |F ) of hypotheses. In
the discriminative training framework of [15], which is usedin most modern SMT systems, scores of machine translation
hypotheses are generally defined as a log-linear combinationof feature functions such as language model or translation
model probabilities
P (E 0 |F ) =1 ∑i wi φi (E 0 ,F )
e
Z

(4)

where φi indicates feature functions such as the language
model, translation model, and reordering model log probabilities, wi is the weight measuring the relative importanceof this feature, and Z is a partition function that ensures thatthe probabilities add to 1.
Choosing the weights wi for each feature such that theanswer with highest probabilityÊ = argmaxE P (E|F )(5)

is the best possible translation is a process called “tuning,”
and essential to modern SMT systems. However, in mosttuning methods, including the standard minimum error rate
training [16] that was used in the proposed system, while therelative weight∑of each feature wi is adjusted, the overall sumof the weights i wi is generally set fixed at 1. While this is
not a problem when finding the highest probability hypothesis in 5, it will affect the probability estimates P (E 0 |F ), with

DecodingViterbi
MBR (λ = 1)
Lattice MBR (λ = 1)
Lattice MBR (λ = 5)

dev2010
27.59
27.29
26.70
27.05

tst2010
31.01
31.24
31.25
31.81

Table 6: BLEU Results using Minimum Bayes Risk decoding.

larger s assigning a larger probability to the most probablehypothesis, and a smaller s spreading the probability mass
more evenly across all hypotheses.
In order to improve the calibration of our probability estimates, and thus improve the performance of MBR, we introduce an addition scaling factor λ into the calculation of ourprobability
P (E 0 |F ) =1 λ ∑i wi φi (E 0 ,F )
e
.
Z

(6)

Using this lambda, we tried every value in 0.1, 0.2, 0.5, 1.0,
2.0, 5.0, and 10.0, and finally chose λ = 5.0, which gave thebest performance on tst2010.
The final results of our system with Viterbi decoding (noMBR), regular MBR over n-best lists, and lattice MBR withthe scaling factors of 1 and 5, are shown in Table 6. It can beseen that both MBR and lattice-based MBR give small improvements over the baseline without tuning λ, while tuning
λ gives a large improvement.3 The reason why MBR reduces the accuracy on dev2010 is because dev2010 was usedin tuning the parameters during MERT, so the one-best answers tend to be better on average than they would be on aheld-out test set.

translation pipeline, and has achieved competitive results on
TED En-Fr [17]. Three conditions were tried: (1) TED-only
data, (2) TED + News (NC), (3) TED + NC + EuroParl (EP).
Results are shown in Table 7.
First, we observe that adding data gives slight improvements (29.32 to 29.57). To analyze the potential for improvement, we also measured BLEU using “CheatLM” decoding[18]. “CheatLM” is an analysis technique for TM adaptation where the language model is trained on the reference;
this gives a optimistic estimate on what can be achieved by
the translation model, if other components are tuned almost
perfectly. Here we see that TED+NC+EP (59.93 BLEU) canachieve large improvements over TEDonly (55.10 BLEU),
indicating the potential value of out-of-domain bitext. However, note that the corresponding OOV rate reduction is relatively small (1.2% to 0.52%). We hypothesize that out-ofdomain probably is not helping because of improved word
coverage, but rather because of improved word alignment estimation. In any case, the improvements are slight so we do
not attempt to draw any further conclusions.
Data
TEDonly
TED+NCTED+NC+EP

standard
29.32
29.43
29.57

CheatLM
55.10
58.64
59.93

force
16%
17%
21%

OOV
1.2%
0.85%
0.52%

Table 7: Translation Model Adaption by simple out-ofdomain data concatenation. The “standard” and “CheatLM”
columns show the BLEU scores on tst2012, using standard
Moses decoding and “CheatLM” decoding. The column“force” shows the percentage of tst2010 sentences that canbe translated into the reference using forced decoding. OOVindicates the token out-of-vocabulary rate.

3. Additional Results on English-FrenchThis section presents additional results obtained on the
English-French track. The results here, for the most part,
did not obtain worthwhile BLEU improvements in preliminary experiments, so we did not include them in the official
system as described in Section 2. Although the systems reported in this section use the same dev and test set as that of
Section 2, the training conditions and system configurations
have slight differences, so the results should not be directlycompared. We include these (negative) results for reference
purposes, in order to aid understanding of the English-French
TED task.

3.2. Word Alignment & Phrase Table Combination
We investigated different alignment tools and ways to combine them, as shown in Table 8. Observations are as follows:• GIZA++ and BerkeleyAligner achieve similar BLEU
on this task.
• Concatenating GIZA++ and BerkeleyAligner wordalignment results, prior to phrase extraction, achievesa small boost (29.57 to 29.89 BLEU).

We experimented with the simplest approach to exploiting
out-of-domain bitext in translation models: data concatenation. This can be seen as adaptation at the earliest stage of the• We also experimented with pilaign [19], a Bayesianphrasal alignment toolkit. This tool directly extracts
phrases without resorting to the preliminary step ofword alignments, and achieves extremely compact
phrase table sizes (0.8M entries) without significantly
sacrificing BLEU (29.24).

3 It should be noted that due to constraints in the available data for these
MBR experiments we are both tuning on testing on tst2010, but the tuningof λ also demonstrated gains in accuracy on the official blind test on tst2011
and tst2012 (37.33→37.90 and 38.92→39.47 respectively).

• Combining the GIZA++ and pialign phrase tables by
Moses’ multiple decoding paths feature did not improve results. Overall, we did not find much differ-

3.1. Exploiting Out-of-domain Dataence among these various approaches so we used thestandard GIZA++ tool chain in the official submission.
Tool
1: GIZA++
2: BerkeleyAligner
3: pialign1+2: ConcatAlign (GIZA,Berkeley)
1+3: TwoTable (GIZA,pialign)

BLEU
29.57
29.39
29.24
29.89
29.56

TableSize
109
170
0.8
200
201

Table 8: BLEU scores on tst2010 of various combinationsof alignment and phrase training tools. TableSize shows thephrase-table size of corresponding method (in millions of
entries). GIZA++ and BerkeleyAligner are trained the the
TED+NC+EP bitext; pialign is trained only on TED, due totime constraints in our preliminary experiments.

# of random restarts
1
10
20
50

Iteration
11
11
12
12

Dev BLEU
28.18
28.17
28.29
28.31

Time (m)
Wall
CPU
0.59
0.82
2.21
17.22
4.91
57.88
9.72 171.91

Table 10: The effect of the number of random restarts in
MERT on BLEU score and multi-threaded time. “Iteration”
denotes the number of iterations which MERT needs to beconverged. “Time” denote the average time of weight optimization for each iteration, averaged over all iterations.
MethodMERT
PRO-basicPRO-interpolated

Dev BLEU
28.29
26.99
27.11

Table 11: Comparison with MERT and PRO. For MERT, thenumber of random restarts was set to 20.
3.3. Lexical Reordering Models
Several reordering models available in the Moses decoder
were tried. In general, we found the full “msd-bidir-fe” option to perform best, despite the small number of word orderdifferences between English and French. Results are shownin Table 9.
Reordering modelmsd-bidir-fe
msd-bidir-fmonotonicity-bidir-fe
msd-backward-fe
distance
msd-bidir-fe-collapse

BLEU
29.57
29.43
29.29
29.22
28.99
28.86

Table 9: Comparison of Reordering models on tst2010.

3.4. MERT vs. PRO tuning
We compared two tuning methods: MERT and PRO [20].
We used the implementations distributed with Moses. For
both MERT and PRO, we set the size of k-best list to k =100, used 14 standard features, and removed duplicates in
k-best lists when merging previously generated k-best lists.
We ran MERT in multi-threaded setting until convergence.
Since the number of random restarts in MERT greatly affects
on the translation accuracy [21], we tried various number ofrandom restarts for 1, 10, 20, and 50.4 For PRO, we used
MegaM5 as a binary classifier with the default setting. We
ran PRO for 25 iterations. We tried two kinds of PRO: [20]interpolated the weights with previously learned weights toimprove the stability (henceforth “PRO-interpolated”)6 , and
4 Currently, Moses’s default setting is 20.
5 http://www.cs.utah.edu/

˜hal/megam/

6 We set the same interpolation coefficient value of 0.1 as [20] noted.

the version that do not use such a interpolation (henceforth“PRO-basic”).
We first investigate the effect of the number of random
restarts in MERT on BLEU score and run-time for each iteration. Table 10 shows the result. As the number of random restarts increases, BLEU score improves. However, therun-time increases as well. We used 20 random restarts tocompare to PRO.
Table 11 shows the results of MERT and PRO. As can beseen in Figure 11, MERT exceeds PRO-basic by 1.3 points
and PRO-interpolated by 1.18 points. As a result, we used
MERT for tuning in Sections 2 and 4.

4. Systems for Translation into English
We participated in the translation of all 10 additional
language-pairs of the TED Talk track. The source languages are Arabic (ar), German (de), Dutch (nl), Polish (pl),Brazilian-Portuguese (pt), Romanian (ro), Russian (ru), Slovak (sk), Turkish (tr), and Chinese (zh). The target language
for all tasks is English (en).
Since all tasks translate into the same language, we areable to share the language model as well as many of theconfigurations for the Experimental Management System(EMS). This setup provides an invaluable chance to compare
the same techniques across structurally-different languages,
and is the focus of our work. Rather than optimizing for specific languages, we concentrate on building common systems
under the same EMS framework and on comparing the performance of existing techniques cross-lingually.
It is interesting to note that the 10 language-pairs cover adiverse range of linguistic phenomenon. In terms of historical relationships, the Italic family (pt,ro) and Germanic family (de, nl) are expected to be closer to the target languageof English. The Slavic family (pl,ru,sk), Arabic, and Turkish

languages exhibit rich morphology (fusional, non-catenative,
or agglutinative). Additionally, the Germanic family may
show word order differences (V2 and SOV) and Chinese requires word segmentation.
4.1. Experiments
Table 12 summarizes all the results (BLEU scores) for translation into English. In all language pairs, the baseline consists of a standard phrase-based Moses system (GIZA++alignment, grow-diag-final-and heuristic, lexical ordering,
4-gram language model) trained on the TED Talks portionof the training data. MERT tuning is performed on the“dev2010” portion of the data and Table 12 shows test results on “tst2010.”7 While it is not possible to directly compare BLEU across languages, we do observe that the Italicand Germanic languages fare better on this TED task (> 25
BLEU), while Chinese, Turkish, and the Slavic languages
perform poorly at 10 − 17 BLEU.
We then proceeded to improve on these baseline results.
First, adding additional out-of-domain data (nc=News Commentary, ep=Europarl, un=UN Multitext) to the language
model increased results uniformly for all language pairs (line(b) of Table 12). We used an interpolated language model,
trained in the same fashion as in our English-French system.
Next, we tried two strategies for handling rich morphology in the input. The “CompoundSplit” program in theMoses package was developed for languages with extensive
noun compounding, e.g. German, and breaks apart words
if sub-parts are seen in the training data over a certain frequency [22]. The alternate “Morfessor” program [23] is anunsupervised morphological analyzer based on the Minimum
Description Length principle – it tries to find the the smallest set of morphemes that parsimoniously cover the training
set. Morfessor is expected to segment more aggressively thanCompoundSplit, especially because it can find both boundand free morphemes. However, we empirically found that
Morfessor segments too aggressively for unknown words(i.e. each character becomes a morpheme), so we do not segment OOV words in dev/test.8 The results in line (c) of Table
12 shows that German benefit most from CompoundSplit,
while Arabic, Russian, and Turkish benefit from Morfessor.
The remaining languages perform approximately equal orslightly better with these morphology enhancements, so infurther experiments we keep the morphology pre-processing(de & ro uses CompoundSplit; others use Morfessor).
In line (d) of Table 12, we further added the Giga corpus to the interpolated language model. For some languages,
this gave a large improvement (ar, de, pl, sk), while for other7 For Slovak, which lacked an official dev/test split, we split the development data, with the first half for tuning and the second half for testing. All
source languages, except for Slovak, have comparable amounts of in-domain
data (130k-145k sentence pairs).
8 In other words, we keep OOV words as is and propagate it to the output.
This implies that we lose the opportunity to translate OOV words whose
component morphemes are seen in the training data. However, we think thisconservative option is safer in the presence of potential over-segmentation.

languages the results remain similar. Some of these results
represent our official submission. In line (e), adding Lattice
MBR decoding uniformly degraded results, so we chose not
to include it. This is in contrast with our English-French results. We suspect that in this case uniformity of the training data and lack of diversity in the n-best list may havedamaged MBR; the resulting translations appear similar in
structure, but many have extraneous articles and determiners, which hurts BLEU. It should also be noted that unlike
English-French, we did not calibrate the probability distribution by adjusting λ, which might also had a significant effecton the results. Finally in line (f), we added additional outof-domain bitext for Translation Model training. This only
helped slightly for pl and tr, while degrading other language
pairs: we conclude that more advanced TM adaptation methods is necessary, and simply concatenating the bitext does
not help.
Finally, we note that our submitted systems for each language achieve a 0.7-2.5 BLEU improvement over the respective baselines. We also achieve slight improvements in
METEOR, despite not tuning for it. While the feature that
helped most depends on language, we observe that morphological pre-processing and larger language models are generally worthwhile efforts.

5. Conclusion
This paper described our experiments with a number of existing machine translation techniques for the IWSLT 2012 TED
task. Some of these techniques, such as minimum Bayesrisk decoding with calibrated probabilities, language modelinterpolation, unsupervised morphology processing, translation model smoothing, and the use of large data proved tobe effective. We also found that a number of techniques, including tuning using PRO, alignment combination, and datafiltering had less of a positive effect.

6. References[1] M. Federico, M. Cettolo, L. Bentivogli, M. Paul, and
S. Stüker, “Overview of the IWSLT 2012 evaluation campaign,” in Proceedings of the International Workshop on Spoken Language Translation (IWSLT), Hong Kong, HK, December 2012.
[2] P. Koehn, H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens,
C. Dyer, O. Bojar, A. Constantin, and E. Herbst, “Moses:
Open source toolkit for statistical machine translation,” inProceedings of the 45th Annual Meeting of the Association
for Computational Linguistics (ACL), 2007, pp. 177–180.
[3] P. Koehn, F. J. Och, and D. Marcu, “Statistical phrase-basedtranslation,” in Proceedings of the Human Language Technology Conference (HLT-NAACL), 2003, pp. 48–54.
[4] D. S. Munteanu and D. Marcu, “Improving machine translation performance by exploiting non-parallel corpora,” Computational Linguistics, vol. 31, no. 4, pp. 477–504, 2005.

SYSTEM(a): baseline(b): (a)+LM:nc,ep,un(c): (b)+morphology
compoundsplit
morfessor(d): (c)+LM:giga(e): (d)+lattice MBR(f): (d)+TM (outdomain)∆bleu: (d) or (f) - (a)∆meteor : (d) or (f) - (a)

ar
21.6
21.9

de
26.8
26.9

nl
30.6
31.4

pl
15.5
15.6

pt
35.6
36.1

ro
28.7
29.2

ru
16.8
17.3

sk
16.8
17.7

tr
12.5
12.6

22.5
23.4
24.1
23.4
21.7
2.5
1.7

27.4
26.8
28.0
27.1
26.2
1.2
0.7

31.2
31.6
31.4
30.7
29.5
0.8
0.2

15.6
15.6
16.2
15.4
16.4
0.9
0.7

36.2
36.3
36.2
34.7
35.3
0.7
0.2

29.1
28.8
29.4
27.6
29.3
0.7
0.3

17.0
17.6
17.5
16.4
16.5
0.8
0.8

17.7
17.7
18.4
17.8
1.6
0.6

12.9
13.6
13.8
13.7
13.9
1.4
1.5

Table 12: BLEU Results for Translations into English. Roughly, each row builds on top of the previous row. Boldface indicatesofficial submission. For zh-en (not shown in table as the segmentation methods are different from other language pairs), the
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