Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pages 539–546,
Sydney, July 2006. c©2006 Association for Computational Linguistics
Stochastic Iterative Alignment for Machine Translation Evaluation
Ding Liu and Daniel Gildea
Department of Computer Science
University of Rochester
Rochester, NY 14627
Abstract
A number of metrics for automatic eval-
uation of machine translation have been
proposed in recent years, with some met-
rics focusing on measuring the adequacy
of MT output, and other metrics focus-
ing on  uency. Adequacy-oriented met-
rics such as BLEU measure n-gram over-
lap of MT outputs and their references, but
do not represent sentence-level informa-
tion. In contrast,  uency-oriented metrics
such as ROUGE-W compute longest com-
mon subsequences, but ignore words not
aligned by the LCS. We propose a metric
based on stochastic iterative string align-
ment (SIA), which aims to combine the
strengths of both approaches. We com-
pare SIA with existing metrics, and  nd
that it outperforms them in overall evalu-
ation, and works specially well in  uency
evaluation.
1 Introduction
Evaluation has long been a stumbling block in
the development of machine translation systems,
due to the simple fact that there are many correct
translations for a given sentence. Human evalu-
ation of system output is costly in both time and
money, leading to the rise of automatic evalua-
tion metrics in recent years. In the 2003 Johns
Hopkins Workshop on Speech and Language En-
gineering, experiments on MT evaluation showed
that BLEU and NIST do not correlate well with
human judgments at the sentence level, even when
they correlate well over large test sets (Blatz et
al., 2003). Liu and Gildea (2005) also pointed
out that due to the limited references for every
MT output, using the overlapping ratio of n-grams
longer than 2 did not improve sentence level eval-
uation performance of BLEU. The problem leads
to an even worse result in BLEU’S  uency eval-
uation, which is supposed to rely on the long n-
grams. In order to improve sentence-level evalu-
ation performance, several metrics have been pro-
posed, including ROUGE-W, ROUGE-S (Lin and
Och, 2004) and METEOR (Banerjee and Lavie,
2005). ROUGE-W differs from BLEU and NIST
in that it doesn’t require the common sequence be-
tween MT output and the references to be consec-
utive, and thus longer common sequences can be
found. There is a problem with loose-sequence-
based metrics: the words outside the longest com-
mon sequence are not considered in the metric,
even if they appear both in MT output and the
reference. ROUGE-S is meant to alleviate this
problem by computing the common skipped bi-
grams instead of the LCS. But the price ROUGE-
S pays is falling back to the shorter sequences and
losing the advantage of long common sequences.
METEOR is essentially a unigram based metric,
which prefers the monotonic word alignment be-
tween MT output and the references by penalizing
crossing word alignments. There are two prob-
lems with METEOR. First, it doesn’t consider
gaps in the aligned words, which is an important
feature for evaluating the sentence  uency; sec-
ond, it cannot use multiple references simultane-
ously.1 ROUGE and METEOR both use WordNet
and Porter Stemmer to increase the chance of the
MT output words matching the reference words.
Such morphological processing and synonym ex-
traction tools are available for English, but are not
always available for other languages. In order to
take advantage of loose-sequence-based metrics
and avoid the problems in ROUGE and METEOR,
we propose a new metric SIA, which is based on
loose sequence alignment but enhanced with the
following features:
1METEOR and ROUGE both compute the score based on
the best reference
539
• Computing the string alignment score based
on the gaps in the common sequence. Though
ROUGE-W also takes into consider the gaps
in the common sequence between the MT
output and the reference by giving more cred-
its to the n-grams in the common sequence,
our method is more  exible in that not only
do the strict n-grams get more credits, but
also the tighter sequences.
• Stochastic word matching. For the purpose
of increasing hitting chance of MT outputs in
references, we use a stochastic word match-
ing in the string alignment instead of WORD-
STEM and WORD-NET used in METEOR
and ROUGE. Instead of using exact match-
ing, we use a soft matching based on the sim-
ilarity between two words, which is trained
in a bilingual corpus. The corpus is aligned
in the word level using IBM Model4 (Brown
et al., 1993). Stochastic word matching is a
uniform replacement for both morphological
processing and synonym matching. More im-
portantly, it can be easily adapted for differ-
ent kinds of languages, as long as there are
bilingual parallel corpora available (which is
always true for statistical machine transla-
tion).
• Iterative alignment scheme. In this scheme,
the string alignment will be continued until
there are no more co-occuring words to be
found between the MT output and any one of
the references. In this way, every co-occuring
word between the MT output and the refer-
ences can be considered and contribute to the
 nal score, and multiple references can be
used simultaneously.
The remainder of the paper is organized as fol-
lows: section 2 gives a recap of BLEU, ROUGE-
W and METEOR; section 3 describes the three
components of SIA; section 4 compares the per-
formance of different metrics based on experimen-
tal results; section 5 presents our conclusion.
2 Recap of BLEU, ROUGE-W and
METEOR
The most commonly used automatic evaluation
metrics, BLEU (Papineni et al., 2002) and NIST
(Doddington, 2002), are based on the assumption
that  The closer a machine translation is to a pro-
mt1: Life is like one nice chocolate in box
ref: Life is just like a box of tasty chocolate
ref: Life is just like a box of tasty chocolate
mt2: Life is of one nice chocolate in box 
Figure 1: Alignment Example for ROUGE-W
fessional human translation, the better it is (Pa-
pineni et al., 2002). For every hypothesis, BLEU
computes the fraction of n-grams which also ap-
pear in the reference sentences, as well as a brevity
penalty. NIST uses a similar strategy to BLEU but
further considers that n-grams with different fre-
quency should be treated differently in the evalu-
ation (Doddington, 2002). BLEU and NIST have
been shown to correlate closely with human judg-
ments in ranking MT systems with different qual-
ities (Papineni et al., 2002; Doddington, 2002).
ROUGE-W is based on the weighted longest
common subsequence (LCS) between the MT out-
put and the reference. The common subsequences
in ROUGE-W are not necessarily strict n-grams,
and gaps are allowed in both the MT output and
the reference. Because of the  exibility, long
common subsequences are feasible in ROUGE-
W and can help to re ect the sentence-wide sim-
ilarity of MT output and references. ROUGE-W
uses a weighting strategy where the LCS contain-
ing strict n-grams is favored. Figure 1 gives two
examples that show how ROUGE-W searches for
the LCS. For mt1, ROUGE-W will choose either
life is like chocolate or life is like box as the LCS,
since neither of the sequences ’like box’ and ’like
chocolate’ are strict n-grams and thus make no dif-
ference in ROUGE-W (the only strict n-grams in
the two candidate LCS is life is). For mt2, there
is only one choice of the LCS: life is of chocolate.
The LCS of mt1 and mt2 have the same length and
the same number of strict n-grams, thus they get
the same score in ROUGE-W. But it is clear to us
that mt1 is better than mt2. It is easy to verify that
mt1 and mt2 have the same number of common 1-
grams, 2-grams, and skipped 2-grams with the ref-
erence (they don’t have common n-grams longer
than 2 words), thus BLEU and ROUGE-S are also
not able to differentiate them.
METEOR is a metric sitting in the middle
of the n-gram based metrics and the loose se-
540
mt1: Life is like one nice chocolate in box
ref: Life is just like a box of tasty chocolate
ref: Life is just like a box of tasty chocolate
mt2: Life is of one nice chocolate in box 
Figure 2: Alignment Example for METEOR
quence based metrics. It has several phases and
in each phase different matching techniques (EX-
ACT, PORTER-STEM, WORD-NET) are used to
make an alignment for the MT output and the ref-
erence. METEOR doesn’t require the alignment to
be monotonic, which means crossing word map-
pings (e.g. a b is mapped to b a) are allowed,
though doing so will get a penalty. Figure 2 shows
the alignments of METEOR based on the same
example as ROUGE. Though the two alignments
have the same number of word mappings, mt2 gets
more crossed word mappings than mt1, thus it will
get less credits in METEOR. Both ROUGE and
METEOR normalize their evaluation result based
on the MT output length (precision) and the ref-
erence length (recall), and the  nal score is com-
puted as the F-mean of them.
3 Stochastic Iterative Alignment (SIA)
for Machine Translation Evaluation
We introduce three techniques to allow more sen-
sitive scores to be computed.
3.1 Modified String Alignment
This section introduces how to compute the string
alignment based on the word gaps. Given a pair
of strings, the task of string alignment is to obtain
the longest monotonic common sequence (where
gaps are allowed). SIA uses a different weighting
strategy from ROUGE-W, which is more  exible.
In SIA, the alignments are evaluated based on the
geometric mean of the gaps in the reference side
and the MT output side. Thus in the dynamic pro-
gramming, the state not only includes the current
covering length of the MT output and the refer-
ence, but also includes the last aligned positions in
them. The algorithm for computing the alignment
score in SIA is described in Figure 3. The sub-
routine COMPUTE SCORE, which computes the
score gained from the current aligned positions, is
shown in Figure 4. From the algorithm, we can
function GET ALIGN SCORE(mt, M, ref, N)
triangleright Compute the alignment score of the MT output mt
with length M and the reference ref with length N
for i = 1; i ≤ M; i = i +1 do
for j = 1; j ≤ N; j = j +1 do
for k = 1; k ≤ i; k = k +1 do
for m = 1; m ≤ j; m = m +1 do
scorei,j,k,m
= max{scorei−1,j,k,m,scorei,j−1,k,m } ;
end for
end for
scorei,j,i,j =
max
n=1,M;p=1,N
{scorei,j,i,j, scorei−1,j−1,n,p
+ COMPUTE SCORE(mt,ref, i, j, n, p)};
end for
end for
return scoreM,N,M,NM ;
end function
Figure 3: Alignment Algorithm Based on Gaps
function COMPUTE SCORE(mt, ref, i, j, n, p)
if mt[i] == ref [j] then
return 1/p(i − n) × (j − p);
else
return 0;
end if
end function
Figure 4: Compute Word Matching Score Based
on Gaps
see that not only will strict n-grams get higher
scores than non-consecutive sequences, but also
the non-consecutive sequences with smaller gaps
will get higher scores than those with larger gaps.
This weighting method can help SIA capture more
subtle difference of MT outputs than ROUGE-W
does. For example, if SIA is used to align mt1
and ref in Figure 1, it will choose life is like box
instead of life is like chocolate, because the aver-
age distance of ’box-box’ to its previous mapping
’like-like’ is less than ’chocolate-chocolate’. Then
the score SIA assigns to mt1 is:
parenleftbigg 1
1 × 1 +
1
1 × 1 +
1√
1 × 2 +
1√
2 × 5
parenrightbigg
×18 = 0.399
(1)
For mt2, there is only one possible alignment,
its score in SIA is computed as:
parenleftbigg 1
1 × 1 +
1
1 × 1 +
1√
1 × 5 +
1√
2 × 3
parenrightbigg
×18 = 0.357
(2)
Thus, mt1 will be considered better than mt2 in
SIA, which is reasonable. As mentioned in sec-
tion 1, though loose-sequence-based metrics give
a better re ection of the sentence-wide similarity
of the MT output and the reference, they cannot
541
make full use of word-level information. This de-
fect could potentially lead to a poor performance
in adequacy evaluation, considering the case that
the ignored words are crucial to the evaluation. In
the later part of this section, we will describe an it-
erative alignment scheme which is meant to com-
pensate for this defect.
3.2 Stochastic Word Mapping
In ROUGE and METEOR, PORTER-STEM and
WORD-NET are used to increase the chance of
the MT output words matching the references.
We use a different stochastic approach in SIA to
achieve the same purpose. The string alignment
has a good dynamic framework which allows the
stochastic word matching to be easily incorporated
into it. The stochastic string alignment can be im-
plemented by simply replacing the function COM-
PUTE SCORE with the function of Figure 5. The
function similarity(word1, word2) returns a ratio
which re ects how similar the two words are. Now
we consider how to compute the similarity ratio of
two words. Our method is motivated by the phrase
extraction method of Bannard and Callison-Burch
(2005), which computes the similarity ratio of two
words by looking at their relationship with words
in another language. Given a bilingual parallel
corpus with aligned sentences, say English and
French, the probability of an English word given
a French word can be computed by training word
alignment models such as IBM Model4. Then for
every English word e, we have a set of conditional
probabilities given each French word: p(e|f1),
p(e|f2), ... , p(e|fN). If we consider these proba-
bilities as a vector, the similarities of two English
words can be obtained by computing the dot prod-
uct of their corresponding vectors.2 The formula
is described below:
similarity(ei, ej) =
Nsummationdisplay
k=1
p(ei|fk)p(ej|fk) (3)
Paraphrasing methods based on monolingual par-
allel corpora such as (Pang et al., 2003; Barzilay
and Lee, 2003) can also be used to compute the
similarity ratio of two words, but they don’t have
as rich training resources as the bilingual methods
do.
2Although the marginalized probability (over all French
words) of an English word given the other English word
(PNk=1 p(ei|fk)p(fk|ej)) is a more intuitive way of measur-
ing the similarity, the dot product of the vectors p(e|f) de-
scribed above performed slightly better in our experiments.
function STO COMPUTE SCORE(mt, ref, i, j, n, p)
if mt[i] == ref [j] then
return 1/p(i − n) × (j − p);
else
return similarity(mt[i],ref [i])√(i−n)×(j−p) ;
end if
end function
Figure 5: Compute Stochastic Word Matching
Score
3.3 Iterative Alignment Scheme
ROUGE-W, METEOR, and WER all score MT
output by  rst computing a score based on each
available reference, and then taking the highest
score as the  nal score for the MT output. This
scheme has the problem of not being able to use
multiple references simultaneously. The itera-
tive alignment scheme proposed here is meant to
alleviate this problem, by doing alignment be-
tween the MT output and one of the available ref-
erences until no more words in the MT output
can be found in the references. In each align-
ment round, the score based on each reference
is computed and the highest one is taken as the
score for the round. Then the words which have
been aligned in best alignment will not be con-
sidered in the next round. With the same num-
ber of aligned words, the MT output with fewer
alignment rounds should be considered better than
those requiring more rounds. For this reason, a
decay factor α is multiplied with the scores of
each round. The  nal score of the MT output is
then computed by summing the weighted scores
of each alignment round. The scheme is described
in Figure 6.
The function GET ALIGN SCORE 1 used
in GET ALIGN SCORE IN MULTIPLE REFS
is slightly different from GET ALIGN SCORE
described in the prior subsection. The dynamic
programming algorithm for getting the best
alignment is the same, except that it has two more
tables as input, which record the unavailable po-
sitions in the MT output and the reference. These
positions have already been used in the prior best
alignments and should not be considered in the
ongoing alignment. It also returns the aligned
positions of the best alignment. The pseudocode
for GET ALIGN SCORE 1 is shown in Figure 7.
The computation of the length penalty is similar
to BLEU: it is set to 1 if length of the MT output
is longer than the arithmetic mean of length of the
542
function GET ALIGN SCORE IN MULTIPLE REFS(mt,
ref 1, ..., ref N , α)
triangleright Iteratively Compute the Alignment Score Based on
Multiple References and the Decay Factor α
final score = 0;
while max score != 0 do
for i = 1, ..., N do
(score, align) =
GET ALIGN SCORE 1(mt, ref i, mt table, ref tablei);
if score > max score then
max score = score;
max align = align;
max ref = i;
end if
end for
final score += max score ×α;
α × = α;
Add the words in align to mt table and
ref tablemax ref ;
end while
return final score× length penalty;
end function
Figure 6: Iterative Alignment Scheme
references, and otherwise is set to the ratio of the
two. Figure 8 shows how the iterative alignment
scheme works with an evaluation set containing
one MT output and two references. The selected
alignment in each round is shown, as well as the
unavailable positions in MT output and refer-
ences. With the iterative scheme, every common
word between the MT output and the reference
set can make a contribution to the metric, and
by such means SIA is able to make full use of
the word-level information. Furthermore, the
order (alignment round) in which the words are
aligned provides a way to weight them. In BLEU,
multiple references can be used simultaneously,
but the common n-grams are treated equally.
4 Experiments
Evaluation experiments were conducted to com-
pare the performance of different metrics includ-
ing BLEU, ROUGE, METEOR and SIA.3 The test
data for the experiments are from the MT evalu-
ation workshop at ACL05. There are seven sets
of MT outputs (E09 E11 E12 E14 E15 E17 E22),
all of which contain 919 English sentences. These
sentences are the translation of the same Chinese
input generated by seven different MT systems.
The  uency and adequacy of each sentence are
manually ranked from 1 to 5. For each MT output,
there are two sets of human scores available, and
3METEOR and ROUGE can be downloaded at
http://www.cs.cmu.edu/ alavie/METEOR and
http://www.isi.edu/licensed-sw/see/rouge
function GET ALIGN SCORE1(mt, ref, mttable, reftable)
triangleright Compute the alignment score of the MT output mt
with length M and the reference ref with length N, without
considering the positions in mttable and reftable
M = |mt|; N = |ref|;
for i = 1; i ≤ M; i = i +1 do
for j = 1; j ≤ N; j = j +1 do
for k = 1; k ≤ i; k = k +1 do
for m = 1; m ≤ j; m = m +1 do
scorei,j,k,m
= max{scorei−1,j,k,m, scorei,j−1,k,m};
end for
end for
if i is not in mttable and j is not in reftable then
scorei,j,i,j = max
n=1,M;p=1,N
{scorei,j,i,j,
scorei−1,j−1,n,p + COMPUTE SCORE(mt, ref, i, j, n, p)};
end if
end for
end for
return scoreM,N,M,NM and the corresponding alignment;
end function
Figure 7: Alignment Algorithm Based on Gaps
Without Considering Aligned Positions
m: England with France discussed this crisis in London
r1: Britain and France consulted about this crisis in London with each other
r2: England and France discussed the crisis in London
m: England with France discussed this crisis in London
r2: England and France discussed the crisis in London
r1: Britain and France consulted about this crisis in London with each other
m: England with France discussed this crisis in London
r1: Britain and France consulted about this crisis in London with each other
r2: England and France discussed the crisis in London
Figure 8: Alignment Example for SIA
543
we randomly choose one as the score used in the
experiments. The human overall scores are calcu-
lated as the arithmetic means of the human  uency
scores and adequacy scores. There are four sets
of human translations (E01, E02, E03, E04) serv-
ing as references for those MT outputs. The MT
outputs and reference sentences are transformed to
lower case. Our experiments are carried out as fol-
lows: automatic metrics are used to evaluate the
MT outputs based on the four sets of references,
and the Pearson’s correlation coef cient of the au-
tomatic scores and the human scores is computed
to see how well they agree.
4.1 N-gram vs. Loose Sequence
One of the problems addressed in this paper is
the different performance of n-gram based metrics
and loose-sequence-based metrics in sentence-
level evaluation. To see how they really differ
in experiments, we choose BLEU and ROUGE-
W as the representative metrics for the two types,
and used them to evaluate the 6433 sentences in
the 7 MT outputs. The Pearson correlation coef -
cients are then computed based on the 6433 sam-
ples. The experimental results are shown in Ta-
ble 1. BLEU-n denotes the BLEU metric with
the longest n-gram of length n. F denotes  u-
ency, A denotes adequacy, and O denotes overall.
We see that with the increase of n-gram length,
BLEU’s performance does not increase monoton-
ically. The best result in adequacy evaluation is
achieved at 2-gram and the best result in  uency is
achieved at 4-gram. Using n-grams longer than 2
doesn’t buy much improvement for BLEU in  u-
ency evaluation, and does not compensate for the
loss in adequacy evaluation. This con rms Liu and
Gildea (2005)’s  nding that in sentence level eval-
uation, long n-grams in BLEU are not bene cial.
The loose-sequence-based ROUGE-W does much
better than BLEU in  uency evaluation, but it does
poorly in adequacy evaluation and doesn’t achieve
a signi cant improvement in overall evaluation.
We speculate that the reason is that ROUGE-W
doesn’t make full use of the available word-level
information.
4.2 METEOR vs. SIA
SIA is designed to take the advantage of loose-
sequence-based metrics without losing word-level
information. To see how well it works, we choose
E09 as the development set and the sentences in
the other 6 sets as the test data. The decay fac-
B-3 R 1 R 2 M S
F 0.167 0.152 0.192 0.167 0.202
A 0.306 0.304 0.287 0.332 0.322
O 0.265 0.256 0.266 0.280 0.292
Table 2: Sentence level evaluation results of
BLEU, ROUGE, METEOR and SIA
tor in SIA is determined by optimizing the over-
all evaluation for E09, and then used with SIA
to evaluate the other 5514 sentences based on the
four sets of references. The similarity of English
words is computed by training IBM Model 4 in
an English-French parallel corpus which contains
seven hundred thousand sentence pairs. For every
English word, only the entries of the top 100 most
similar English words are kept and the similarity
ratios of them are then re-normalized. The words
outside the training corpus will be considered as
only having itself as its similar word. To com-
pare the performance of SIA with BLEU, ROUGE
and METEOR, the evaluation results based on
the same testing data is given in Table 2. B-
3 denotes BLEU-3; R 1 denotes the skipped bi-
gram based ROUGE metric which considers all
skip distances and uses PORTER-STEM; R 2 de-
notes ROUGE-W with PORTER-STEM; M de-
notes the METEOR metric using PORTER-STEM
and WORD-NET synonym; S denotes SIA.
We see that METEOR, as the other metric
sitting in the middle of n-gram based metrics
and loose sequence metrics, achieves improve-
ment over BLEU in both adequacy and  uency
evaluation. Though METEOR gets the best re-
sults in adequacy evaluation, in  uency evaluation,
it is worse than the loose-sequence-based metric
ROUGE-W-STEM. SIA is the only one among
the 5 metrics which does well in both  uency and
adequacy evaluation. It achieves the best results in
 uency evaluation and comparable results to ME-
TEOR in adequacy evaluation, and the balanced
performance leads to the best overall evaluation
results in the experiment. To estimate the signif-
icance of the correlations, bootstrap resampling
(Koehn, 2004) is used to randomly select 5514
sentences with replacement out of the whole test
set of 5514 sentences, and then the correlation co-
ef cients are computed based on the selected sen-
tence set. The resampling is repeated 5000 times,
and the 95% con dence intervals are shown in Ta-
bles 3, 4, and 5. We can see that it is very dif -
544
BLEU-1 BLEU-2 BLEU-3 BLEU-4 BLEU-5 BLEU-6 ROUGE-W
F 0.147 0.162 0.166 0.168 0.165 0.164 0.191
A 0.288 0.296 0.291 0.285 0.279 0.274 0.268
O 0.243 0.256 0.255 0.251 0.247 0.244 0.254
Table 1: Sentence level evaluation results of BLEU and ROUGE-W
low mean high
B-3 (-16.6%) 0.138 0.165 0.192 (+16.4%)
R 1 (-17.8%) 0.124 0.151 0.177 (+17.3%)
R 2 (-14.3%) 0.164 0.191 0.218 (+14.2%)
M (-15.8%) 0.139 0.166 0.191 (+15.5%)
S (-13.3%) 0.174 0.201 0.227 (+13.3%)
Table 3: 95% signi cance intervals for sentence-
level  uency evaluation
low mean high
B-3 (-08.2%) 0.280 0.306 0.330 (+08.1%)
R 1 (-08.5%) 0.278 0.304 0.329 (+08.4%)
R 2 (-09.2%) 0.259 0.285 0.312 (+09.5%)
M (-07.3%) 0.307 0.332 0.355 (+07.0%)
S (-07.9%) 0.295 0.321 0.346 (+07.8%)
Table 4: 95% signi cance intervals for sentence-
level adequacy evaluation
cult for one metric to signi cantly outperform an-
other metric in sentence-level evaluation. The re-
sults show that the mean of the correlation factors
converges right to the value we computed based on
the whole testing set, and the con dence intervals
correlate with the means.
While sentence-level evaluation is useful if we
are interested in a con dence measure on MT out-
puts, syste-x level evaluation is more useful for
comparing MT systems and guiding their develop-
ment. Thus we also present the evaluation results
based on the 7 MT output sets in Table 6. SIA uses
the same decay factor as in the sentence-level eval-
uation. Its system-level score is computed as the
arithmetic mean of the sentence level scores, and
low mean high
B-3 (-09.8%) 0.238 0.264 0.290 (+09.9%)
R 1 (-10.2%) 0.229 0.255 0.281 (+10.0%)
R 2 (-10.0%) 0.238 0.265 0.293 (+10.4%)
M (-09.0%) 0.254 0.279 0.304 (+08.8%)
S (-08.7%) 0.265 0.291 0.316 (+08.8%)
Table 5: 95% signi cance intervals for sentence-
level overall evaluation
WLS WLS WLS WLS
PROB INCS PROB
INCS
F 0.189 0.202 0.188 0.202
A 0.295 0.310 0.311 0.322
O 0.270 0.285 0.278 0.292
Table 7: Results of different components in SIA
WLS WLS WLS WLS
INCS INCS INCS INCS
STEM WN STEM
WN
F 0.188 0.188 0.187 0.191
A 0.311 0.313 0.310 0.317
O 0.278 0.280 0.277 0.284
Table 8: Results of SIA working with Porter-Stem
and WordNet
so are ROUGE, METEOR and the human judg-
ments. We can see that SIA achieves the best per-
formance in both  uency and adequacy evaluation
of the 7 systems. Though the 7-sample based re-
sults are not reliable, we can get a sense of how
well SIA works in the system-level evaluation.
4.3 Components in SIA
To see how the three components in SIA con-
tribute to the  nal performance, we conduct exper-
iments where one or two components are removed
in SIA, shown in Table 7. The three components
are denoted as WLS (weighted loose sequence
alignment), PROB (stochastic word matching),
and INCS (iterative alignment scheme) respec-
tively. WLS without INCS does only one round
of alignment and chooses the best alignment score
as the  nal score. This scheme is similar to
ROUGE-W and METEOR. We can see that INCS,
as expected, improves the adequacy evaluation
without hurting the  uency evaluation. PROB
improves both adequacy and  uency evaluation
performance. The result that SIA works with
PORTER-STEM and WordNet is also shown in
Table 8. When PORTER-STEM and WordNet are
545
B-6 R 1 R 2 M S
F 0.514 0.466 0.458 0.378 0.532
A 0.876 0.900 0.906 0.875 0.928
O 0.794 0.790 0.792 0.741 0.835
Table 6: Results of BLEU, ROUGE, METEOR and SIA in system level evaluation
both used, PORTER-STEM is used  rst. We can
see that they are not as good as using the stochastic
word matching. Since INCS and PROB are inde-
pendent of WLS, we believe they can also be used
to improve other metrics such as ROUGE-W and
METEOR.
5 Conclusion
This paper describes a new metric SIA for MT
evaluation, which achieves good performance by
combining the advantages of n-gram-based met-
rics and loose-sequence-based metrics. SIA uses
stochastic word mapping to allow soft or partial
matches between the MT hypotheses and the ref-
erences. This stochastic component is shown to
be better than PORTER-STEM and WordNet in
our experiments. We also analyzed the effect of
other components in SIA and speculate that they
can also be used in other metrics to improve their
performance.
Acknowledgments This work was supported
by NSF ITR IIS-09325646 and NSF ITR IIS-
0428020.
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