N-gram-based Tense Models for Statistical Machine Translation

N-gram-based Tense Models for Statistical Machine Translation
N-gram-based Tense Models for Statistical Machine Translation
Zhengxian Gong1 Min Zhang2 Chewlim Tan3 Guodong Zhou1∗
School of Computer Science and Technology, Soochow University, Suzhou, China 215006
Human Language Technology, Institute for Infocomm Research, Singapore 138632
School of Computing, National University of Singapore, Singapore 117417
{zhxgong, gdzhou}@suda.edu.cn [email protected] [email protected]
Tense is a small element to a sentence, however, error tense can raise odd grammars and
result in misunderstanding. Recently, tense
has drawn attention in many natural language
processing applications. However, most of
current Statistical Machine Translation (SMT)
systems mainly depend on translation model
and language model. They never consider and
make full use of tense information. In this paper, we propose n-gram-based tense models
for SMT and successfully integrate them into a state-of-the-art phrase-based SMT system
via two additional features. Experimental results on the NIST Chinese-English translation
task show that our proposed tense models are
very effective, contributing performance improvement by 0.62 BLUE points over a strong
For many NLP applications, such as event extraction
and summarization, tense has been regarded as a key
factor in providing temporal order. However, tense
information has been largely overlooked by current
SMT research. Consider the following example:
SRC: ù ‘ B$ ´ eÔ
, • ™ ‡N ¥I † î† m
(J , Ø U ‡N y3
ºŠ 'X "
REF:The embargo is a result of the Cold War and does not
reflect the present situation nor the partnership between China
and the EU.
MOSES: the embargo is the result of the cold war, not reflect
the present situation, it did not reflect the partnership with the
european union.
Although the translated text produced by Moses1
is understandable, it has very odd tense combination
from the grammatical aspect, i.e. with tense inconsistency (is/does in REF vs. is/did in Moses). Obviously, slight modification, such as changing “is”
into “was”, can much improve the readability of the
translated text. It is also interesting to note that such
modification can much affect the evaluation. If we
change “did” to “does”, the BLEU-4 score increases
from 22.65 to 27.86 (as matching the 4-gram “does
not reflect the” in REF). However, if we change “is”
to “was”, the BLEU score decreases from 22.65 to
The above example seems special. To testify its
impact on SMT in wider range, we design a special
experiment based on the 2005 NIST MT data (see
section 6.1). This data has 4 references. We choose
one reference and modify its sentences with error
tense2 . After that, we use other 3 references to measure this reference. The modified reference leads to
a sharp drop in BLEU-4 score, from 52.46 to 50.27
in all. So it is not a random phenomenon that tense
can affect translation results.
The key is how to detect tense errors and choose
correct tenses during the translation procedure. By
carefully comparing the references with Moses output, we obtain the following useful observations,
Observation(1): to most simple sentences, coordinate verbs should be translated with the same tense
while they have different tense in Moses output;
Observation(2): to some compound sentences,
Such changes are small by mainly modifying one auxiliary
verb for a sentence, such as “is → was”, “has → had”.
*Corresponding author.
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural
Language Learning, pages 276–285, Jeju Island, Korea, 12–14 July 2012. 2012
Association for Computational Linguistics
the subordinate clause should have the consistent tense with its main clause while Moses fails;
Observation(3): the diversity of tense usage in a
document is common. However, in most cases, the
neighbored sentences tends to share the same main
tense. In some extreme examples, one tense (past or
present), even dominates the whole document.
One possible solution to model above observations is using rules. Dorr (2002) refers to six basic English tense structures and defines the possible
paired combinations of “present, past, future”. But
the practical cases are very complicated, especially in news domain. There are a lot of complicated sentences in news articles. Our preliminary investigation shows that such six paired combinations
can only cover limited real cases in Chinese-English
This paper proposes a simple yet effective method
to model above observations. For each target sentence in the training corpus, we first parse it and extract its tense sequence. Then, a target-side tense
n-gram model is constructed. Such model can be
used to estimate the rationality of tense combination in a sentence and thus supervise SMT to reduce
tense inconsistency errors against Observations (1)
and (2) in the sentence-level. In comparison, Observation (3) actually reflects the tense distributions
among one document. After extracting each main
tense for each sentence, we build another tense ngram model in the document-level. For clarity, this
paper denotes document-level tense as “inter-tense”
and sentence-level tense as “intra-tense”.
After that, we propose to integrate such tense
models into SMT systems in a dynamic way. It
is well known there are many errors in the current
MT output (David et al., 2006). Unlike previously
making trouble with reference texts, the BLEU-4 score cannot be influenced obviously by modifying
a small part of abnormal sentences in a static way.
In our system, both inter-tense and intra-tense model are integrated into a SMT system via additional
features and thus can supervise the decoding procedure. During decoding, once some words with correct tense can be determined, with the help of language model and other related features, the small component–“tense”–can affect surrounding words
and improve the performance of the whole sentence.
Our experimental results (see the examples in Sec277
tion 6.4) show the effectiveness of this way.
Rather than the rule-based model, our models are
fully statistical-based. So they can be easily scaled
up and integrated into either phrase-based or syntaxbased SMT systems. In this paper, we employ a
strong phrase-based SMT baseline system, as proposed in Gong et al. (2011), which uses document as
translation unit, for better incorporating documentlevel information.
The rest of this paper is organized as follows: Section 2 reviews the related work. Section 3 and 4 are
related to tense models. Section 3 describes the preprocessing work for building tense models. Section
4 presents how to build target-side tense models and
discuss their characteristics. Section 5 shows our
way of integrating such tense models into a SMT
system. Session 6 gives the experimental results. Finally, we conclude this paper in Section 7.
Related Work
In this section, we focus on related work on integrating the tense information into SMT. Since both interand intra-tense models need to analyze and extract
tense information, we also give a brief overview on
tense prediction (or tagging).
Tense Prediction
The tense prediction task often needs to build a model based on a large corpus annotated with temporal
relations and thus its focus is on how to recognize,
interpret and normalize time expressions. As a representative, Lapata and Lascarides (2006) proposed
a simple yet effective data-intensive approach. In
particular, they trained models on main and subordinate clauses connected with some special temporal marker words, such as “after” and “before”, and
employed them in temporal inference.
Another typical task is cross-lingual tense predication. Some languages, such as English, are inflectional, whose verbs can express tense via certain
stems or suffix, while others, such as Chinese often lack inflectional forms. Take Chinese to English
translation as example, if Chinese text contains particle word “ (Le)”, the nearest Chinese verb prefers
to be translated into English verb with the past tense.
Ye and Zhang (2005), Ye et al. (2007) and Liu et al.
(2011) focus on labeling the tenses for keywords in
source-side language.
Ye and Zhang (2005) first built a small amount of manually-labeled data, which provide the tense
mapping from Chinese text to English text. Then,
they trained a CRF-based tense classifier to label
tense on Chinese documents. Ye et al. (2007) further reported that syntactic features contribute most
to the marking of aspectual information. Liu et al.
(2011) proposed a parallel mapping method to automatically generate annotated data. In particular,
they used English verbs to label tense information
for Chinese verbs via a parallel Chinese-English corpus.
In this paper, tense modeling is done on the targetside language. Since our experiments are done
on Chinese to English SMT, our tense models are
learned only from the English text. In the literature,
the taxonomy of English tenses typically includes
three basic tenses (i.e. present, past and future) plus
their combination with the progressive and perfective aspects. Here, we consider four basic tenses:
present, past, future and UNK (unknown) and ignore
the aspectual information. Furthermore, we assume
that one sentence has only one main tense but maybe
has many subordinate tenses.
This section describes the preprocessing work of
building tense models, which mainly involves how
to extract tense sequence via tense verbs.
It is reasonable to label such source-side verb to
supervise the translation process since the tense of
English sentence is often determined by verbs. The
problem is that due to the diversity of English verb inflection, it is difficult to map such Chinese tense
information into the English text. To our best knowledge, although above works attempt to serve for
SMT, all of them fail to address how to integrate
them into a SMT system.
Machine Translation with Tense
Dorr (1992) described a two-level knowledge representation model based on Lexical Conceptual Structures (LCS) for machine translation which integrates
the aspectual information and the lexical-semantic
information. Her system is based on an inter-lingual
model and does not belong to a SMT system.
Olsen et al. (2001) relied on LCS to generate
appropriately-tensed English translations for Chinese. In particular, they addressed tense reconstruction on a binary taxonomy (present and past) for
Chinese text and reported that incorporating lexical
aspect features of telicity can obtain a 20% to 35%
boost in accuracy on tense realization.
Ye et al. (2006) showed that incorporating latent
features into tense classifiers can boost the performance. They reported the tense resolution results
based on the best-ranked translation text produced
by a SMT system. However, they did not report the
variation of translation performance after introducing tense information.
Preprocessing for Tense Modeling
Tense Verbs
Lapata et al.(2006) used syntactic parse trees to find
clauses connected with special aspect markers and
collected them to train some special classifiers for
temporal inference. Inspired by their work, we use
the Stanford parser3 to parse tense sequence for each
Take the following three typical sentences as examples, (a) is a simple sentence which contains two
coordinate verbs, while (b) and (c) are compound
sentences and (b) contains a quoted text.
(a) Japan’s constitution renounces the right to go to war and
prohibits the nation from having military forces except for selfdefense.
(b) “We also hope Hong Kong will not be affected by diseases
like the severe acute respiratory syndrome again!” , added Ms.
(c) Cheng said he felt at home in Hong Kong and he sincerely
wished Hong Kong more peaceful and more prosperous.
Figure 1 shows the parse tree with Penn Treebank
style for each sentence, which has strict level structures and POS tags for all the terminal words. Here,
the level structures mainly contribute to extract main
tense for each sentence (to be described in Section
3.2) and POS tags are utilized to detect tense verbs,
i.e. verbs with tense information.
Normally, POS tags in the parse tree can distinguish five different forms of verbs: the base form
(tagged as VB), and forms with overt endings D for
Figure 1: The Stanford parse trees with Penn Treebank style
past tense, G for present participle, N for past participle, and Z for third person singular present. It is
worth noting that VB, VBG and VBN cannot determine the specific tenses by themselves. In addition,
the verbs with POS tag “MD” need to be specially considered to distinguish future tense from other
Algorithm 1 illustrates how to determine what
tense a node has. If the return value is not “UNK”,
the node belongs to a tense verb.
Algorithm 1 Determine the tense of a node.
The TreeNode of one parse tree, leaf node;
The tense, tense;
1: tense = “U N K 00
2: Obtaining the POS tag lpostag from leaf node;
3: Obtaining the word lword from leaf node;
4: if (lpostag in [“V BP 00 , “V BZ 00 ]) then
tense = “present00
6: else if (lpostag == “V BD 00 ]) then
tense = “past00
8: else if (lpostag == “M D 00 ]) then
if (lword in [“will00 , “ll00 , “shall00 ]) then
tense = “f uture00
else if (lword in [“would00 , “could00 ]) then
tense = “past00
tense = “present00
end if
16: end if
17: return tense;
Tense Extraction Based on Tense Verbs
As described in Section 1, the inter-tense
(document-level) refers to the main tense of
one sentence and the intra-tense (sentence-level)
corresponds to all tense sequence of one sentence.
This section introduces how to recognize the main
tense and extract all useful tense sequence for each
The idea of determining the main tense is to find
the tense verb located in the top level of a parse tree.
According to the Penn Treebank style, the method
to determine the main tense can be described as follows:
Traverse the parse tree top-down until a tree node
containing more than one child is identified, denoted as Sm .
(2) Consider each child of Sm with tag “VP”, recursively traverse such “VP” node to find a tense verb. If
found, use it as the main tense and return the tense;
if not, go to step (3).
(3) Consider each child of Sm with tag “S”, which actually corresponds to subordinate clause of this sentence. Starting from the first subordinate clause, apply the similar policy of step (2) to find the tense
verb. If not found, search remaining subordinate
(4) If no tense verb found, return “UNK” as the main
Here, “VP” nodes dominated by Sm directly are
preferred over those located in subordinate clauses.
This is to ensure that the main tense is decided by
the top-level tense verb.
Take Figure 1 as an example, the main tense of
sentence (a) and (b) can be determined only by step
(2). The tense verb of “(VBZ renounces)” dominated in the “VP” tag determines that (a) is in present
tense. Similarly the node “(VBD added)” indicates
that (b) is in past tense. Sentence (c) needs to be further treated by step (3) since there is no “VP” nodes
dominated by Sm directly. The node “(VBD said)”
located in the first subordinate clause shows its main
tense is “past”.
The next task is to extract the tense sequence for
each sentence. They are determined by all tense
verbs in this sentence according to the strict topdown order. For example, the tense sequence of
sentence (a), (b) and (c) are {present, present},
{present, future, past} and {past, past, past}. In order to explore whether the main tense of intra-tense
model has an impact on SMT or not, we introduce
a special marker “*” to denote the main tense. So
the tense sequence marked with main tense of (a),
(b) and (c) are {present*, present},{present, future,
past*} and {past*, past, past}. It is worth noting, the
intra-tense model (see Section 4) based on the latter
tense sequence is different to the former.
N-gram-based Tense Models
Tense N-gram Estimation
After applying the previous method to extract tense
for an English text corpus, we can obtain a big tense
Given the current tense is indexed as ti , we call
the previous n − 1 tenses plus the current tense as
tense n-gram.
Based on the tense corpus, tense n-gram statistics
can be done according to the Formula 1.
P (ti |t(i−(n−1)) , ..., t(i−1)) =
count(t(i−(n−1)) , . . . , t(i−1) , ti )
count(t(i−(n−1)) , ..., t(i−1) )
Here, the function of “count” return the tense n-gram
frequency. In order to avoid doing specific smoothing work, we estimate tense n-gram probability using SRI language modeling (SRILM) tool (Stolcke,
To compute the probability of intra-tense n-gram,
we first extract all tense sequence for each sentence
and put them into a new file. Based on this new file,
we can get the intra-tense n-gram model via SRILM
To compute the probability of inter-tense n-gram,
we need to extract the main tense for each sentence
at first. Then, for each document, we re-organized
the main tenses of all sentences into a special line.
After putting all these special lines into a new file,
we can use SRILM to obtain the inter-tense n-gram
Characteristic of Tense N-gram Models
We construct n-gram-based tense models on English
Gigaword corpus (LDC2003T05). This corpus is
used to build language model for most SMT systems. It includes 30221 documents (we remove such
files: file size is less than 1K or the number of continuous “UNK” tenses is greater than 5).
Figure 2 shows the unigram and bigram probabilities (Log10-style) for intra-tense and inter-tense.
The part (a) and (c) in Figure 2 refer to unigram.
The horizontal axis indicts tense type, and the vertical axis shows its probabilities. The parts (a) and
(c) also indicate “present” and “past” are two main
tense types in news domain.
The part (b) and (d) refer to bigram. The horizontal axis indicts history tense. Each different colorful bar indicts one current tense. The vertical axis
shows the transfer possibilities from a history tense
to a current tense.
The part (b)4 reflects transfer possibilities of tense
types in one sentence. It also slightly reflects some
linguistic information. For example, in one sentence, the probability of co-occurrence of “present
→ present”, “past → past” and “future → present”
is more than other combinations, which can be against tense inconsistency errors described in Observation (1) and (2) (see Section 1). However, it seems strange that “present → past” exceeds “present →
future”. We checked our corpus and found a lot of
sentences like this–“the bill has been . . . , he said. ”.
The part (d) shows tense type can be switched between two neighbored sentences. However, it shows
the strong tendency to use the same tense type for
The co-occurrence of the “UNK” tense and other tense
types in one sentence cannot happen, so the “UNK” tense is
Figure 2: statistics of intra-tense and inter-tense N-gram
neighbored sentences. This statistics conform to the
previous observation (3) very much.
Integrating N-gram-based Tense Models
into SMT
In this section, we discuss how to integrate the previous tense models into a SMT system.
Basic phrase-based SMT system
It is well known that the translation process of SMT
can be modeled as obtaining the best translation e
of the source sentence f by maximizing following
posterior probability(Brown et al., 1993):
ebest = arg max P (e|f )
= arg max P (f |e)Plm (e)
where P (e|f ) is a translation model and Plm is a
language model.
Our baseline is a modified Moses, which follows
Koehn et al. (2003) and adopts similar six groups
of features. Besides, the log-linear model ( Och and
Ney, 2000) is employed to linearly interpolate these
features for obtaining the best translation according
to the formula 3:
ebest = arg max
λm hm (e, f )
where hm (e, f ) is a feature function, and λm is
the weight of hm (e, f ) optimized by a discriminative training method on a held-out development data(Och, 2003).
first obtains tense sequence for such hypothesis and
computes intra-tense feature Fs (see Section 5.3). At
the same time, it recognizes the main tense of this
hypothesis and associate the main tense of previous
sentence to compute inter-tense feature Fm (see Section 5.3).
Next, the decoder uses such two additional feature
values to re-score this hypothesis automatically and
choose one hypothesis with highest score as the final
After translating one sentence, the decoder caches
its main tense and pass it to the next sentence.
When one document has been processed, the decoder cleans this cache.
In order to successfully implement above workflow, we should firstly design some related features,
then resolve another key problem of determining
tense (especially main tense) for SMT output. They
are described in Section 5.3 and 5.4 respectively.
Two Additional Features
Although the previous tense models show strong
tendency to use the consistent tenses for one sentence or one document, other tense combinations also can be permitted. So we should use such models
in a soft and dynamic way. We design two features:
inter-tense feature and intra-tense feature. And the
weight of each feature is tuned by the MERT script
in Moses packages.
Given main tense sequence of one document t1 , . . . , tm , the inter-tense feature Fm is calculated
according to the following formula:
Fm =
The Workflow of Our System
P (ti |ti−(n−1) , . . . , t(i−1) )
Our system works as follows:
When a hypothesis has covered all source-side
words during the decoding procedure, the decoder
The P (·) of formula 4 can be estimated by the formula 1. It is worth noting the first sentence of one
document often scares tense information since it corresponds to the title at most cases. To the first sentence, the P (·) value is set to 14 (4 tense types).
Given tense sequence of one sentence
s1 , . . . , se (e > 1), the intra-tense feature Fs
is calculated as follows:
u e
Fs = t P (si |si−(n−1) , . . . , s(i−1) )
Here the square-root operator is used to avoid punishing translations with long tense sequence. It is
worth noting if the sentence only contains one tense,
the P (·) value of formula 5 is also set to 41 .
Since the average length of intra-tense sequence
is about 2.5, we mainly consider intra-tense bigram
model and thus n equals to 2. 5
Determining Tense For SMT Output
The current SMT systems often produce odd translations partly because of abnormal word ordering and
uncompleted text etc. For these abnormal translated
texts, the syntactic parser cannot work well in our
initial experiments, so the previous method to parse
main tense and tense sequence of regular texts cannot be applied here too.
Fortunately, the solely utilization of Stanford POS
tagger for our SMT output is not bad although it has
the same issues described in Och et al. (2002). The
reason may be that phrase-based SMT contains short
contexts that POS tagger can utilize while the syntax
parser fails.
Once obtaining a completed hypothesis, the decoder will pass it to the Stanford POS tagger and according to tense verbs to get all tense sequence for
this hypothesis. However, since the POS tagger can
not return the information about level structures, the
decoder cannot recognize the main tense from such
tense sequence.
Liu et al. (2011) once used target-side verbs to label tense of source-side verbs. It is natural to consider whether Chinese verbs can provide similar clues
in an opposite direction.
Since Chinese verbs have good correlation with
English verbs (described in section 6.2), we obtain
In our experiment, the intra-tense bigram model can obtain the comparable performance to the trigram model. And the
inter-tense trigram model can not exceed the bigram one.
Figure 3: trees for parallel sentences
main tense for SMT output according to such tense
verb, which corresponds to the “VV”(Chinese POS
labels are different to English ones, “VV” refers to
Chinese verb) node in the top level of the source-side
parse tree. Take Figure 3 as an example, the English
node “(VBD announced)” is a tense verb which can
tell the main tense for this English sentence. The
Chinese verb “(VVú Ù)” in the top-level of the
Chinese parse tree is just the corresponding part for
this English verb.
So, before translating one sentence, the decoder
first parses it and records the location of one Chinese
“VV” node which located in the top-level, denotes
this location as Sarea .
Once a completed hypothesis is generated, according to the phrase alignment information, the decoder can map Sarea into the English location Tarea
and obtain the main tense according to the POS tags
in Tarea .
If Tarea does not contain tense verb, such as the
verb POS tags in the list of {VB, VBN, VBG},
which cannot tell tense type by themselves, our system permits to find main tense in the left/right 3
words neighbored to Tarea . And the proportion that
the top-level verb of Chinese has a verb correspondence in English can reach to 83% in this way.
Experimental Setting for SMT
In our experiment, SRI language modeling toolkit was used to train a 5-Gram general language
model on the Xinhua portion of the Gigaword corpus. Word alignment was performed on the training parallel corpus using GIZA++ ( Och and Ney,
2000) in two directions. For evaluation, the NIST
BLEU script (version 13) with the default setting is
used to calculate the BLEU score (Papineni et al.,
2002), which measures case-insensitive matching of
4-grams. To see whether an improvement is statistically significant, we also conduct significance tests
using the paired bootstrap approach (Koehn, 2004).
In this paper, “***” and “**” denote p-values equal
to 0.05, and bigger than 0.05, which mean significantly better, moderately better respectively.
Experimental Results
All the experiment results are showed on the table 3.
Our Baseline is a modified Moses. The major modification is input and output module in order to translate using document as unit. The performance of our
baseline exceeds the baseline reported by Gong et al.
(2011) about 2 percent based on the similar training
and test corpus.
Table 1: Corpus statistics
We use FBIS as the training data, the 2003 NIST
MT evaluation test data as the development data, and
the 2005 NIST MT test data as the test data. Table 1
shows the statistics of these data sets (with document
boundaries annotated).
We found Chinese verbs have more than 77% possibilities to align to English verbs in total. However,
our method will fail when some special Chinese sentences only contain noun predicates.
Moses Md(Baseline)
Baseline+Fs (∗)
Baseline+Fm +Fs
Baseline+Fm +Fs (*)
BLEU on Test(%)
28.87(***) 8.7935
28.88(***) 8.7987
28.92(***) 8.8201
The Correlation of Chinese Verbs and
English Verbs
Table 3: The performance of using different feature combinations
In this section, an additional experiment is designed
to show English Verbs have close correspondence
with Chinese Verbs.
We use the Stanford POS tagger to tag the Chinese and English sentences in our training corpus
respectively at first. Then we utilize Giza++ to build
alignment for these special Word-POS pairs. According to the alignment results, we find the corresponding relation for some special POS tags in two
The system denoted as “Baseline+Fm ” integrates
the inter-tense feature. The performance boosts
0.57(***) in BLEU score.
The system denoted as “Baseline+Fs ” integrates
the intra-tense feature into the baseline. The improvement is less than the inter-tense model, only 0.31(**). It seems the tenses in one sentence
has more flexible formats than the document-level
ones. It is worth noting, this method can gain higher performance on the develop data than the one of
“Baseline+Fm ” while fail to improve the test data.
Maybe the related weight is tuned over-fit.
The system denoted as “Baseline+Fs (*)” is slightly different from “Baseline+Fs ”. This experiment is to check whether the main tense has an impact on intra-tense model or not (see Section 3.2).
Here, the intra-tense model based on the tense sequence with main tense marker is slightly different
to the model showed in Figure 2. The results are
slightly better than the previous system by 0.13.
Finally, we use the two features together
(Baseline+Fm +Fs ). The best way improved the
performance by 0.62(***) in BLEU score over our
Chinese Verb POS
English POS
Verb VBD
In sum:
Other Non-Verb
Verb Corresponding Ratio
Table 2: The Chinese and English Verb Pos Alignment
The “Number” column of Table 2 shows the numbers of Chinese words with “VV” tag corresponding to English words with different verb POS tags.
1)±Ú ô¬ K µ£ ˜ ^ ̇ ´ , |Æ ô”« ; ½Â
ôÂ "
2)nVd" )˜ |„ +E Cnd • ¼O c
à ÜW
¢ Ë|ð , ù ´ L o c 5 ±Ú
Û Ä g ON nV
d" -‡ + <Ô ë\ ‹ ! Ÿ; "
1)Israeli settlers blockaded a major road to protest a mortar attack
on the settlement area.
2)PLO leader Abbas had also been allowed to go to the West Bank
town of Bethlehem , which is the first time in the past four years
Israeli authorities have allowed a senior Palestinian leader to attend
Christmas celebrations.
1)israel has imposed a main road to protest by mortars attack .
2)the palestinian leader also visited the west bank cities and towns
to bethlehem , which in the past four years , the israeli authorities
allowed the palestinian leading figures attended the ceremony .
1)israel has imposed a main road to protest against the mortars attack .
2)leader of the palestinian liberation organization have also been
allowed to go to the west bank towns , bethlehem in the past four
years . this is the first time the israeli authorities allow palestinian
leading figures attended the ceremony .
Table 4 shows special examples whose intra-tenses
are changed in our proposed system. The example 1 and 2 show such modification can improve
the BLEU score but the example 3 obtains negative
impact. From these examples, we can see not only
tense verbs have changed but also their surrounding
words have subtle variation.
Translation sentence
Baseline: the gulf countries , the bahraini royal family members by the military career of part of the
banned to their marriage stories like children , have
become the theme of television films .
19.71 Ours: the gulf country is a member of the bahraini
royal family , a risk by military career risks part of
the banned to their marriage like children , has become a story of the television and film industry .
17.16 Baseline:economists said that the sharp appreciation
of the euro , in the recent investigation continues to
have an impact on economic confidence , it is estimated that the economy is expected to rebound to
pick up .
24.25 Ours: economists said that the sharp appreciation of
the euro , in the recent investigation continued to
have an impact on economic confidence and therefore no reason to predict the economy expected to
pick up a rebound .
73.03 Baseline: the middle east news agency said that , after the concerns of all parties concerned in the middle east peace process , israel and palestine , egypt ,
the united states , russia and several european countries will hold a meeting in washington .
72.95 Ours: the middle east news agency said that after the
concerns of all parties in the middle east peace process , israel and palestine , egypt , the united states ,
russia and several european countries held a meeting
in washington .
Table 4: Examples with tense variation using intra-tense
From the results showed on Table 3, the
document-level tense model seems more effective
than the sentence-level one. We manually choose
and analyzed 5 documents with significant improvement in the test data. The part (a) of Figure 4 shows
the main tense distributions of one reference. The
main tense distributions for the baseline and our proposed system are showed in the part (b) and (c) respectively. These documents have different numbers
of sentences but all less than 10. The vertical axis indicates different tense: 1 to “past”, 2 to “present”, 3
to “future” and 4 to “UNK”. It is obvious that our
system has closer distributions to the ones of this
The examples in Table 5 indicate the joint impact
of inter-tense and intra-tense model on SMT. Sen284
Table 5: the joint impact of inter- tense and intra-tense
models on SMT
tence 1) and 2) are two neighbored sentences in one
document. Both the reference and our output tend
to use the same main tense type, but the former is in
“past” tense and the latter is in “present” tense. The
baseline cannot show such tendency. Although our
main tense is different to the reference one, the consistent tenses in document level bring better translation results than the baseline. And the tenses in
sentence level also show better consistency than the
This paper explores document-level SMT from the
tense perspective. In particular, we focus on how to
build document-level and sentence-level tense models and how to integrate such models into a popular
SMT system.
Compared with the inter-tense model which greatly improves the performance of SMT, the intra-tense
model needs to be further explored. The reasons are
many folds, e.g. the failure to exclude quoted texts
when modeling intra-tense, since tenses in quoted
texts behave much diversely from normal texts. In
the future work, we will focus on modeling intratense variation according to specific sentence types
and using more features to improve it.
Figure 4: the comparison of the inter-tense distributions for reference, baseline and our proposed system
This research is supported by part by NUS FRC
Grant R252-000-452-112, the National Natural Science Foundation of China under grant No.90920004
and 61003155, the National High Technology Research and Development Program of China (863
Program) under grant No.2012AA011102.
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