A Co-Chunk based method

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A CO-CHUNK BASED METHOD
FOR SPOKEN-LANGUAGE TRANSLATION1
CHENG Wei2, ZHAO Jun, LIU Feifan and XU Bo
National Laboratory of Pattern Recognition
Institute of Automation, Chinese Academy of Sciences, Beijing, China
[email protected] , [email protected] , [email protected] , [email protected]
ABSTRACT
 More flexible speech styles: slow or rapid speeches
Chunking is a useful step for natural language
processing. The paper puts forward a definition of cochunks for Chinese-English spoken-language translation,
based on both the characteristics of spoken-language and
the differences between Chinese and English. An
algorithm is proposed to identify the co-chunks
automatically, which combines the rules into a statistical
method and makes a co-chunk has both syntactical
structure and perfect meaning. Using the co-chunk
alignment corpus, we present the framework of our
translation system. In the framework, the word-based
translation mode is employed to smooth the co-chunkbased translation model. A series of experiments show
that the proposed definition and the co-chunking method
can lead to great improvement to the quality of the
Chinese-English spoken-language translation.
KEYWORDS
chunking; spoken-language
machine translation.
translation;
statistical
1. INTRODUCTION
It is well known that speech-to-speech translation faces
more problems in comparison with pure text translation,
such as:
 More irregular spoken utterances: There are much
more pauses, repetitions, omitting etc. in spoken
language.
1
2
with different stresses, accent, appears.
 No punctuation to segment the utterances.
Confronted with these problems, more robust
technologies are needed to be developed to achieve an
acceptable performance in the spoken-language
translation system. In recent years, some data-driven
methods are taken as the effectual ways for machine
translation, such as the example-based machine
translation (EBMT, proposed by ATR) and the statistical
machine translation (SMT). The statistical approach is an
adequate framework for introducing automatic learning
techniques in spoken-language translation. It has been
studied for many years[1][2][3][4][5]. However, its
performance isn’t very satisfactory[6].
In this paper, we introduce text chunking into the SMT
model to improve the translation quality. Chunking is a
useful step for natural language processing. There are
many researches dealing with chunk parsing for single
language[7][8][9]. However, in machine translation, we need
a definition correlative with both the source language and
the target language. Therefore, we first present the cochunk definition for Chinese-English spoken-language
translation. Then a co-chunking method based on the
definition is investigated. Finally a SMT system based on
co-chunks is built to improve the translation quality.
The paper is organized as follows. Section 2 describes the
definition and the features of the co-chunk. Section 3
presents an automatic algorithm for the co-chunk
identification. And section 4 presents a statistical
translation framework based on the co-chunk. In section 5
experimental results are presented and analyzed. Some
remarks are given in section 6.
This work is sponsored by the Natural Sciences Foundation of China under grant No. 60272041, 60121302, 60372016
This author is now working in the Artificial Intelligence Laboratory of Beijing City University.
3)
2. DEFINITION OF CO-CHUNKS
In this paper, a co-chunk is composed of a source subchunk and a target sub-chunk. Each of them has both the
syntactic structure and the low ambiguous meaning. The
definition can be described by the following formula:
BC  { bs, bt | bs  ws0 ,, wsl , bt  wt0 ,, wt m ;
bs  bt; wsi  wsl0 , wt j  wt0m ;
(1)
l  [0, NS ], m  [0, NT ]}
Where, BC denotes a set of co-chunks. “bs” is the source
sub-chunk and l is its length. wsi is a word in the source
sentence. “bt” is the target sub-chunk and m is its length.
wti is a word in the target sentence. NS is the number of
source sub-chunks in the source sentence. And NT is the
number of target sub-chunks in the target sentence. The
detailed explanations are as follows.
1)
2)
3)
Meaning. The typical sub-chunk consists of a single
content word and its contextual environment.
Therefore the meaning of the sub-chunk is the less
ambiguous. This definition can be used for
disambiguation in the machine translation.
Transition. Meanwhile, the meaning of the target
sub-chunk should be as the same as that of the
corresponding source sub-chunk, except that a
source sub-chunk is corresponding to a null target
sub-chunk and vice versa.
NP
VP
NP
ADVP
VP
ADJP
PUN
两 人 || 住 || 这 房间 || 可 || 是 || 小 了 点儿 || 。
I am afraid || this room || is || too small || for two || .
ID
NP
VP
ADJP
PP
PUN
Fig. 1 An example of Chinese-English co-chunk
2)
3. THE AUTOMATIC IDENTIFICATION OF
CO-CHUNKS
Figure 2 gives the process of the automatic identification
of the co-chunks. It includes three parts: 1) source
chunking, 2) searching the target chunks according to the
source chunks, 3) proof-checking of the co-chunks.
Structure. The sub-chunk is defined as a syntactic
structure which can be described as a connected subgraph of the sentence’s parse-tree. None of them in a
sentence overlaps each other.
An example of Chinese-English co-chunk is given in
figure 1. From it we can see some features of the cochunk:
1)
It integrates the syntactic rules of two different
languages. Here we define 8 kinds of basic subchunks for Chinese as noun sub-chunk, verb subchunk, interrogative sub-chunk, adjective subchunk, preposition sub-chunk, adverb sub-chunk,
modal/punctuation sub-chunk and idiom sub-chunk.
While in English, a SBAR sub-chunk is added and
the modal/punctuation sub-chunk is renamed as the
interjection sub-chunk. The definitions of all kinds
of the sub-chunk are according to the character of
both the Chinese and the English.
It builds the semantic relation between two
languages.
It keeps one of the characteristics in the most
definitions of monolingual chunk, that is, chunks
have a legal syntax structure. Therefore, we can use
the shallow analysis to extract the co-chunk.
The automatic identification system of co-chunks
Bilingual
sentences
Parsing by Possible Search for Possible Proofsource chunk source co-chunks co-chunks checking
co-chunks
chunks
Rules of the
source language
Stochastic
parameter
Rules of the
target language
Bilingual corpse
Fig. 2 Structure of the identification system for co-chunks
3.1. Searching for co-chunk
The finite state machine (FSM) can be employed in the
stages of source chunking and proof-checking. Dynamic
programming together with heuristic function is used in
the searching for the co-chunks. The search algorithm is
as follows.
1)
OPEN := (s), g(s) := 0;
2)
LOOP: IF OPEN = () THEN EXIT (FAIL);
3)
n := FIRST(OPEN);
4)
IF END OF SENTENCE THEN EXIT (SUCCESS);
5)
REOMOVE (n, OPEN), ADD(n, CLOSED);
6)
EXPAND(n)--->{ml}.
7)
IF CHUNK(ml) follows syntactic rules, ADD(ml,
OPEN), and tag POINTER(ml, n);
f ( n, ml )  g ( n, ml )  h(ml ) ;
8)
SAVE min f(PATHi), SORT(NODEj);
9)
GOTO step 2).
f ( k )  g ( k )  h( k )
 [ log p(bt k | bs k )  log p(bt rest | bs rest)]
(9)
k
3.2. Calculation algorithm
4. THE CO-CHUNK BASED TRANSLATION
We define
g (k )    log p(btk | bsk )
(2)
k
Where, bsk is the source sub-chunk of the kth co-chunk
and btk is the target sub-chunk of the kth co-chunk. The
objective of the search can be described as
Fig.3 gives the structure of the translation system based
on the co-chunks. It includes two steps:
1)
Training: First, some preprocessing steps are applied
to the Chinese-English corpus, such as, sentence
segmentation and word segmentation. Then the
identification system is employed to identify the cochunks in the corpus automatically. Therefore, the
statistical models can be trained with the co-chunkbased corpus.
2)
Translation: This step consists of the chunk
matching and the translation decoding. The chunk
matching is similar to Chinese word segmentation. It
can be done by the maximum matching algorithm
according to a Chinese chunking corpus. The
translation decoding is the co-chunk-based SMT
whose unit is not a word but a co-chunk.
K
min{   log p (bt k | bs k )}
(3)
k 1
S  bs1 bs K ; T  bt1 bt K
According to Bayesian formula
p( )  p (bs k | bt k )
p (bt k | bs k )  bt k
p (bs k )
(4)
Where p(bsk) and p(btk) can be estimated by the bigram
language model as
mk
p(bs k )   p( ws j | ws j 1);
j 1
(5)
lk
p(bt k )   p( wt i | wt i 1)
I TRAINING
i 1
p(bsk|btk) is the translation probability of the source subchunk on condition that the target sub-chunk occurs.
mk l k
p(bs k | bt k )  p(l k | m k )    p( ws j | wt i )
(6)
j 1i 1
Where,
can
p ( ws j | wt i )
be
estimated
by
EM
[10]
. p(l k | mk ) is the probability of length
and can be estimated by Possion distribution.
algorithm
Hence, estimation from start node to middle node k is
Bilingual
corpse
Test
sentence
Word-based
corpse
Identification system Chinese Chinese chunk
Chunks
of the co-chunks
matching
Co-chunk
based corpse
Model training
lk
II TRANSLATION
Preprocessing
Parameters
g ( k )  {  log p( wti | wti 1)  log p(l k | mk )
Statistical Translation
machine
results
translation
i 1
k
mk
lk
j 1
i 1
  log  p( ws j | wti )
(7)
Fig. 3 Structure of the SMT system based on co-chunks
mk
  log p( ws j | ws j 1)]}
4.1. Co-chunk-based translation model
j 1
On the other hand,
h(k )   log p(bt rest | bsrest)
NT
NS
NT
 p( wti | wti 1)    p( ws j | wti )
  log
i k 1
j k 1 i k 1
NS
 p( ws j | ws j 1)
j k 1
Thus, we get
(8)
In statistical opinions, translation task can be described as
follow. Given a source (“ Chinese ”) string
C : c1M  wc1 , wc2 ,  wc M , we choose the string E* among
all
possible
target
(“
English
”)
strings
E : e1L  we1, we2 ,, weL with the highest probability that is
given by Bayes’ decision rule [1]
E*  arg max {Pr( e1L | c1M )}
e1L
 arg max {Pr( e1L )  Pr( c1M | e1L )}
e1L
(10)
This is the word-based SMT approach. Pr( e1L ) is the
probability of the language model produced by the target
language. Pr( c1M | e1L ) is the probability of the string
translation model from the target language to the source
language. The argmax operation denotes the decoding
problem, i.e. the generation of the output sentence in the
target language.
In our system, a dynamic programming algorithm is used
as the decoding method which is the same as the fast stack
decoder[11].
Then we define the sentences as
In this section, some results of the automatic identification
system for the co-chunks are presented. A corpus of
66061 sentence pairs is used to train the parameters. A
close test set includes 2487sentences. And the open test
set includes 845 sentences. The precision and the recall is
defined as
N
N
precision  r  100 %; recall  r  100 % (15)
Np
Na
C : bc1J
bc j  wc1, wc2 ,;
E : be1I
bei  we1, we2 ,
Where, bcj is a Chinese chunk, bei is an English chunk. J
is the co-chunk number in the Chinese sentence. It is the
co-chunk number in the English sentence. Because a
source sub-chunk can correspond to a null target subchunk, J isn’t always as same as I. Then the equation 10
can be rewritten as
(11)
E*  arg max P(be1I )  P(bc1J | bt1I )
E
As the word-based SMT, P(be1I ) is the probability of the
5. EXPERIMENTS AND DISCUSSION
5.1. Experiment of the co-chunk identification
Where N p is the co-chunk number of the identification
result. N a is the co-chunk number of the answers. And
N r is the co-chunk number of the right identification.
Table 1 Results of the co-chunk identification
Test set
Closed Test
Open Test
83.86
81.20
Precision (%)
84.65
81.19
Recall (%)
co-chunk language model. P (bc1J | bt1I ) is the probability
of the co-chunk translation model.
4.2. Smoothing
Because the unit number of co-chunk-based system is
larger than that of the word-based system, the data
sparseness problem is a severe problem for the co-chunkbased translation. That is to say, it needs to be smoothed
in both the co-chunk language model and the co-chunk
translation model.
In our system, the trigram model is used as the co-chunk
language model.
I
P( E )  p(be1) p(be2 | be1)  p(bei | bei 2 bei 1)
(12)
i 3
Table 1 shows that the automatic identification method
can deal with parallel corpus effectively. The following
are some analysis and advices for improving the
performances.
1)
2)
3)
And its smoothing algorithm is the back-off method.
Moreover, the co-chunk translation model just likes the
model 1 of IBM[10].
P (C | E )  P (bc1J | be1I )


J
I
  p(bc j | bei )
( I 1) J j 0 i 0
(13)
 presents some small, fixed number. p (bc j | bei ) is the
translation probability of bcj given bei. It can be estimated
from the EM algorithm. And we smooth this model
according to the word-based translation model.
~
p (bc | be) 
p(bc | be)  0
 p(bc | be)
 
m n

p
(
|
)
  wc j wei p(bc | be)  0
 ( n 1)m j 0 i 0

(14)
4)
Accuracy rate and callback rate reach 84.5%
simultaneously for close testing. In the open test its
performance degrades to about 80% which is still
attractive for machine translation.
Most errors are caused by mapping errors between
Chinese chunks and English chunks.
Probability parameters are not accurate enough, due
to the sparse training data we used. It is another
source of mapping errors.
Error rate can be alleviated if more training data are
employed.
Table 2 Examples of the identification results
Examples
麻烦 您 (4)|| 把 预约 (3)|| 推迟 (2)|| 到 三 天 后 (1)|| 。
please (4)|| postpone (2)|| my reservation (3)|| for three days (1)|| .
预定 (10)|| 是 (9)|| 住 (8)|| 两 个 晚上 (7)|| , (6)|| 但 (5)|| 想 (4)||
改为 (3)|| 住 (2)|| 三 个 晚上 (1)|| 。
I (4)|| had (8)|| a reservation (10)|| for (2)|| two nights (7)|| , (6)||
but (5)|| please (-1)|| change (3)|| it (9)|| to three nights (1)|| .
我 (7)|| 今天 (6)|| 订 了 房间 (5)|| 但是 (4)|| 突然 (3)|| 有 了 (2)||
急事 (1)|| 。
I (7)|| have a reservation (5)|| for tonight (6)|| but (4)|| due to (2)||
urgent business (1)|| I am unable (3)|| to make it (-1)|| .
Three examples of identification results are laid out in
Table 2. The numbers in the table are the specific number
of the co-chunks in the sentences.
3)
By formalizing the co-chunks definition, it is
possible to find the better balance point of the
statistical and rule-based methods.
Table 4 Results of the co-chunk-based translation
5.2. Experiment of the co-chunk-based translation
These experiments are carried out on a Chinese-English
parallel corpus. The corpus consists of spontaneous
utterances from hotel reservation dialogs. Although this
task is a limited-domain task, it is difficult for several
reasons: first, the syntactic structures of the sentences are
less restricted and highly variable; second, it covers a lot
of spontaneous speech characters, such as hesitations,
repetitions and corrections. The summary of the corpus is
given in the tables 3.
Table 3 Training corpus
Chinese
Sentences
English
2655
Vocabulary Size
1237
932
Chunk List Size
2785
1775
The system is tested by the test set of 1000 sentences and
evaluated by both subjective judgments and the automatic
evaluation algorithm.
1)
2)
Subjective judgment. The performance measure of
the subjective judgment is the indication of the
closeness of the output to the original with four
grades: (A) All contents of the source sentence are
conveyed perfectly. (B) The contents of the source
sentence are generally conveyed, but some
unimportant details are missing or awkwardly
translated. (C) The contents are not adequately
conveyed. Some important expressions are missing
and the meaning of the output is not clear. (D)
Unacceptable translation or no translation is given.
Automatic evaluation. An automatic evaluation
approach is employed to measure the output quality
of the spoken-language translation. The equation 16
describes its final score. And the detail is in the
reference [12].
N (  2  1)  precision  recalli
i
score  F  
/ N (16)
i 1
 2  precisioni  recalli
Table 4 shows the results of the examination. From it we
can see:
1)
Co-chunk-based model outperforms word-based
alignment model significantly.
2)
In spoken language, the processing unit for human
maybe is chunks rather than words.
training
corpse
Automatic
evaluation
word-based
Co-chunkbased
Subjective judgments (%)
A
B
C
D
0.589
29.2
22.9
33.3
14.6
0.794
66.7
22.9
10.4
0.01
Three examples of the experiments are laid out as follows.
Here, <c> is the Chinese sentence; <tw>is the translation
result of the word-based system; and <tb> is the
translation result of the co-chunk-based system.
Exp1: <c> 靠 河边 风景 漂亮 的 房间 有没有 ?
<tw> any of the river from the room good view
<tb> are there any rooms with a good view of the
river ?
Exp2: <c> 没有 收到 日本 佐藤 来 的 房间 预约 吗 ?
<tw> [Fail. No translation]
<tb> in the name of Sato from Japan ?
Exp3: <c> 只要 带有 淋浴 的 房间 都 行 。
<tw> is that all the rooms with a shower .
<tb> all the rooms with a shower will be fine .
6. CONCLUSION
In this paper, we give a brief overview on recent progress
of our work. These are mainly based on the definition of
the co-chunk according to the spoken-language translation.
A novel co-chunk identification algorithm in SMT
framework is described in detail. Experimental results
show that it can identify the co-chunks effectively. Then a
series of co-chunk-based statistical machine translation
experiments are presented which show that the proposed
definition can lead to great improvement to the quality of
the Chinese-English spoken-language translation.
7. REFERENCES
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approach to word alignment. Computational Linguistics,
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[3] Y. Wang. Grammar inference and statistical machine
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1998
[4] H. Ney, S. Nieβen, F. J. Och, et al. Algorithms for
statistical translation of spoken language. IEEE Trans. on
Speech and Audio Processing, 2000, 8(1): 24-36
[5] Cheng Wei and Xu Bo. Statistical Approach to
Chinese-English Spoken-language Translation in Hotel
Reservation Domain. The International Symposium of
Chinese Spoken Language Processing (ISCSLP’00), 2000.
271-274
[6] M. Carl, “A model of competence for corpus-based
machine translation,” In: Proceedings of COLING’2000,
Saarbrücken, Germany, 2000.
[7] Steven Abney, “Parsing by Chunks,” In: Robert
Berwick, Steven Abney and Carol Tenny (eds.),
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[8] Erik F. Tjong Kim Sang and Sabine Buchholz,
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[9] Zhou Q, Sun Mao-S, and Huang Chang-N, “Chunk
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[10] P. F. Brown, V. J. Della Pietra, S. A. Della Pietra,
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