acl acl2010 acl2010-201 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Xiangyu Duan ; Min Zhang ; Haizhou Li
Abstract: The pipeline of most Phrase-Based Statistical Machine Translation (PB-SMT) systems starts from automatically word aligned parallel corpus. But word appears to be too fine-grained in some cases such as non-compositional phrasal equivalences, where no clear word alignments exist. Using words as inputs to PBSMT pipeline has inborn deficiency. This paper proposes pseudo-word as a new start point for PB-SMT pipeline. Pseudo-word is a kind of basic multi-word expression that characterizes minimal sequence of consecutive words in sense of translation. By casting pseudo-word searching problem into a parsing framework, we search for pseudo-words in a monolingual way and a bilingual synchronous way. Experiments show that pseudo-word significantly outperforms word for PB-SMT model in both travel translation domain and news translation domain. 1
Reference: text
sentIndex sentText sentNum sentScore
1 But word appears to be too fine-grained in some cases such as non-compositional phrasal equivalences, where no clear word alignments exist. [sent-5, score-0.262]
2 Pseudo-word is a kind of basic multi-word expression that characterizes minimal sequence of consecutive words in sense of translation. [sent-8, score-0.308]
3 By casting pseudo-word searching problem into a parsing framework, we search for pseudo-words in a monolingual way and a bilingual synchronous way. [sent-9, score-0.736]
4 Experiments show that pseudo-word significantly outperforms word for PB-SMT model in both travel translation domain and news translation domain. [sent-10, score-0.266]
5 But there is a deficiency in such manner that word is too finegrained in some cases such as non-compositional phrasal equivalences, where clear word alignments do not exist. [sent-15, score-0.262]
6 No clear word alignments are there in such phrasal equivalences. [sent-17, score-0.232]
7 Moreover, should basic translational unit be word or coarsegrained multi-word is an open problem for optimizing SMT models. [sent-18, score-0.297]
8 Some researchers have explored coarse- 少 grained translational unit for machine translation. [sent-19, score-0.22]
9 Marcu and Wong (2002) attempted to directly learn phrasal alignments instead of word alignments. [sent-20, score-0.232]
10 (2008) used synchronous ITG (Wu, 1997) and constraints to find non-compositional phrasal equivalences, but they suffered from intractable estimation problem. [sent-23, score-0.246]
11 (2008; 2009) induced phrasal synchronous grammar, which aimed at finding hierarchical phrasal equivalences. [sent-25, score-0.352]
12 Another direction of questioning word as basic translational unit is to directly question word segmentation on languages where word boundaries are not orthographically marked. [sent-26, score-0.415]
13 (2008) used a Bayesian semi-supervised method that combines Chinese word segmentation model and Chinese-to-English translation model to derive a Chinese segmentation suitable for machine translation. [sent-30, score-0.236]
14 Since there are many 1-to-n phrasal equivalences in Chinese-to-English translation (Ma and Way. [sent-35, score-0.267]
15 2009), only focusing on Chinese word as basic translational unit is not adequate to model 1-to-n translations. [sent-36, score-0.297]
16 Ma and Way (2009) tackle this problem by using word aligner to bootstrap bilingual segmentation suitable for machine translation. [sent-37, score-0.301]
17 c As2s0o1c0ia Atisosnoc foiart Cionom fopru Ctaotmiopnuatla Lti on gaulis Lti cnsg,u piasgtiecs 148–156, pressions by monotonically segmenting a given Spanish-English sentence pair into bilingual units, where word aligner is also used. [sent-40, score-0.313]
18 , 1993) and Deng and Byrne (2005) are another kind of related works that allow 1-to-n alignments, but they rarely questioned if such alignments exist in word units level, that is, they rarely questioned word as basic translational unit. [sent-42, score-0.465]
19 This paper focuses on determining the basic translational units on both language sides without using word aligner before feeding them into PBSMT pipeline. [sent-44, score-0.36]
20 We call such basic translational unit as pseudo-word to differentiate with word. [sent-45, score-0.267]
21 Pseudo-word searching problem is the same to decomposition of a given sentence into pseudowords. [sent-47, score-0.421]
22 We use a measurement, which characterizes pseudo-word as minimal sequence of consecutive words in sense of translation, as potential function in Gibbs distribution. [sent-49, score-0.261]
23 Note that the number of decomposition of one sentence into pseudo-words grows exponentially with sentence length. [sent-50, score-0.263]
24 By fitting decomposition problem into parsing framework, we can find optimal pseudo-word sequence in polynomial time. [sent-51, score-0.346]
25 Then we feed pseudo-words into PB-SMT pipeline, and find that pseudo-words as basic translational units improve translation performance over words as basic translational units. [sent-52, score-0.558]
26 Further experiments of removing the power of higher order language model and longer max phrase length, which are inherent in pseudowords, show that pseudo-words still improve translational performance significantly over unary words. [sent-53, score-0.416]
27 This paper is structured as follows: In section 2, we define the task of searching for pseudowords and its solution. [sent-54, score-0.363]
28 2 Searching for Pseudo-words Pseudo-word searching problem is equal to decomposition of a given sentence into pseudowords. [sent-57, score-0.45]
29 We assume that the distribution of such decomposition is in the form of Gibbs distribution as below: P(Y|X)=Z1Xexp(∑kSigyk) (1) where X denotes the sentence, Y denotes a decomposition of X. [sent-58, score-0.424]
30 Given X, ZX is fixed, so searching for optimal decomposition is as below: Yˆ=ARGYMAXP(Y|X)=ARGY1KMAX∑kSigyk (2) where Y1K denotes K multi-word units from decomposition of X. [sent-61, score-0.648]
31 A multi-word sequence with maximal sum of Sig function values is the search target — pseudo-word sequence. [sent-62, score-0.259]
32 In this paper Sig function calculates sequence significance which is proposed to characterize pseudo-word as minimal sequence of consecutive words in sense of translation. [sent-64, score-0.467]
33 The detail of sequence significance is described in the following section. [sent-65, score-0.255]
34 1 Sequence Significance Two kinds of definitions of sequence significance are proposed. [sent-67, score-0.255]
35 X and Y are monolingual sentence and monolingual multi-words respectively in this monolingual scenario. [sent-69, score-0.524]
36 X and Y are sentence pair and multi-word pairs respectively in this bilingual scenario. [sent-71, score-0.232]
37 We also denote word sequence wi, wj as span[i, j], whole sentence as span[1, n]. [sent-75, score-0.212]
38 Monolingual sequence significance of span[i, j] is proportional to span[i, j]’s frequency, while is inversely proportion to frequency of expanded span (span[i-1, j+1]). [sent-77, score-0.456]
39 Such definition characterizes minimal sequence of consecutive words which we are looking for. [sent-78, score-0.261]
40 Our target is to find pseudo-word sequence which has maximal sum j …, …, …, of spans’ significances: 149 pw1K = ARGspManA1KX ∑ Kk=1 Sig span k (4) where pw denotes pseudo-word, K is equal to or less than sentence’s length. [sent-79, score-0.555]
41 Details of searching algorithm are described in section 2. [sent-83, score-0.231]
42 We firstly search for monolingual pseudowords on source and target side individually. [sent-86, score-0.353]
43 We argue that word alignment techniques will work fine if nonexistent word alignments in such as noncompositional phrasal equivalences have been filtered by pseudo-words. [sent-88, score-0.388]
44 2 Bilingual Sequence Significance Bilingual sequence significance is proposed to characterize pseudo-word pairs. [sent-91, score-0.255]
45 Co-occurrence of sequences on both language sides is used to define bilingual sequence significance. [sent-92, score-0.33]
46 Given a bilingual sequence pair: span-pair[is, js, it, jt] (source side span[is, js] and target side span[it, jt]), bilingual sequence significance is defined as below: Sigis,js,it,jt=FreFqrise− 1q,isjs,+js 1, i t ,− j1t,jt+1 (5) where Freq denotes the frequency of a span-pair. [sent-93, score-0.905]
47 Bilingual sequence significance is an extension of monolingual sequence significance. [sent-94, score-0.553]
48 Pseudo-word pairs of one sentence pair are such pairs that maximize the sum of span-pairs’ bilingual sequence significances: pwp1K=AspRanG−paMir1KAX∑Kk=1Sigspan−pairk( 6) pwp represents pseudo-word pair. [sent-96, score-0.423]
49 Searching for pseudo-word pairs pwp1K is equal to bilingual segmentation of a sentence pair into optimal span-pair1K. [sent-98, score-0.348]
50 Details of searching algorithm are presented in section 2. [sent-99, score-0.231]
51 2 Algorithms words of Searching for Pseudo- Pseudo-word searching problem is equal to decomposition of a sentence into pseudo-words. [sent-103, score-0.45]
52 But the number of possible decompositions of the sentence grows exponentially with the sentence length in both monolingual scenario and bilingual scenario. [sent-104, score-0.547]
53 By casting such decomposition problem into parsing framework, we can find pseudo-word sequence in polynomial time. [sent-105, score-0.327]
54 According to the two scenarios, searching for pseudo-words can be performed in a monolingual way and a synchronous way. [sent-106, score-0.531]
55 Details of the two kinds of searching algorithms are described in the following two sections. [sent-107, score-0.231]
56 1 Algorithm of Searching for Monolingual Pseudo-words (SMP) Searching for monolingual pseudo-words is based on the computation of monolingual sequence significance. [sent-110, score-0.458]
57 In this algorithm, Wi, j records maximal sum of monolingual sequence significances of sub spans of span[i, j]. [sent-117, score-0.63]
58 During initialization, Wi, is initialized as Sigi,i (note that this sequence is word wi only). [sent-118, score-0.326]
59 For span[i, j], Wi, j is updated if higher sum of monolingual sequence significances is found. [sent-121, score-0.516]
60 After maximal sum of significances is found in small spans, big span’s computation, which uses small spans’ maximal sum, is continued. [sent-124, score-0.354]
61 Maximal sum of significances for whole sentence (W1,n, n is sentence’s length) is guaranteed in this way, and optimal decomposition is obtained correspondingly. [sent-125, score-0.437]
62 After steps 3-6, all possible decompositions of span[i, j] are explored and Wi, j of optimal decomposition of span[i, j] is recorded. [sent-127, score-0.265]
63 Then monolingual sequence significance Sigi,j of span[i, j] is computed at step 7, and it is compared to Wi, j at step 8. [sent-128, score-0.415]
64 2 Algorithm of Synchronous Searching for Pseudo-words (SSP) Synchronous searching for pseudo-words utilizes bilingual sequence significance. [sent-133, score-0.531]
65 What it cares about is the span-pairs that maximize the sum of bilingual sequence significances. [sent-136, score-0.353]
66 Initialization: if is = js or it = jt then Wis,js,it,jt Sig is ,js,it,jt else Wis,js,it,jt 0 = = ; ; 1: for ds = 2 … ns, dt = 2 … nt do 2: for all is, js, it, jt s. [sent-137, score-0.915]
67 js-is=ds-1 and jt-it=dt-1 do 3: for ks = is js – 1, kt = it jt – 1 do … 4: … v = max{Wis,ks,it,kt +Wks+ 1,js,kt+ 1,jt Wis,ks,kt+1,jt + Wks+ 1,js,it,kt } , 5: 6: if v > Wis,js,it,jt then Wis,js,it,jt = v; 7: u = Sigis,js,it,jt 8: if u > Wis,js,it ,jt then = 9: Wis,js,it,jt u; Figure 2. [sent-139, score-0.612]
68 In the algorithm, Wis,js,it,jt records maximal sum of bilingual sequence significances of sub span-pairs of span-pair[is, js, it, jt]. [sent-141, score-0.586]
69 For 1-to-m span-pairs, Ws are initialized as bilingual sequence significances of such span-pairs. [sent-142, score-0.502]
70 In the main algorithm, ds/dt denotes the length of a span on source/target side, ranging from 2 to ns/nt (source/target sentence’s length). [sent-144, score-0.318]
71 For span-pair[is, js, it, jt], Wis,js,it,jt is updated at step 6 if higher sum of bilingual sequence significances is found. [sent-148, score-0.518]
72 Fitting the bilingually searching for pseudowords into ITG framework is located at steps 7-9. [sent-149, score-0.453]
73 Then bilingual sequence significance of span-pair[is, js, it, jt] is computed at step 7. [sent-151, score-0.417]
74 Update is taken at step 9 if bilingual sequence significance of span-pair[is, js, it, jt] is bigger than Wis,js,it,jt , which indicates that span-pair[is, js, it, jt] is non-decomposable. [sent-153, score-0.417]
75 In addition to the initialization step, all spanpairs’ bilingual sequence significances are computed. [sent-155, score-0.508]
76 Maximal sum of bilingual sequence sig- nificances for one sentence pair is guaranteed through this bottom-up way, and the optimal decomposition of the sentence pair is obtained correspondingly. [sent-156, score-0.668]
77 151 Initialization: if is = js or it = jt then Wis,js,it,jt Sig is,js,it,jt else = ; = ; Wis,js,it,jt 0 1: for ds = 2 … ns, dt = 2 … nt do 2: for all is, js, it, jt s. [sent-166, score-0.915]
78 We can see that in Figure 4, each monolingual span is configured into three parts, for example: span[is, ks1-1], span[ks1, ks2] and span[ks2+1, js] on source language side. [sent-172, score-0.361]
79 Bilingual sequence significance is computed only on pairs of blank boxes, solid boxes are excluded in this computation to represent NULL alignment cases. [sent-174, score-0.469]
80 Generally, span length of NULL alignment is not very long, so we can set a length threshold for NULL alignments, eg. [sent-179, score-0.358]
81 2 Pseudo-word Unpacking Because pseudo-word is a kind of multi-word expression, it has inborn advantage of higher language model order and longer max phrase length over unary word. [sent-221, score-0.325]
82 The advantage of longer max phrase length is removed during phrase extraction, and the advantage of higher order of language model is also removed during decoding since we use language model trained on unary words. [sent-225, score-0.307]
83 Performances of pseudoword unpacking are reported in section 3. [sent-226, score-0.22]
84 Ma and Way (2009) used the unpacking after phrase extraction, then re-estimated phrase translation probability and lexical reordering model. [sent-231, score-0.338]
85 pwchpwen denotes that pseudo-words are on both language side of training data, and they are input strings during development and testing, and translations are also pseudo-words, which will be converted to words as final output. [sent-235, score-0.221]
86 This shows that excluding NULL alignments in synchronous searching for pseudowords is effective. [sent-238, score-0.599]
87 ESSP is superior to SMP indicating that bilingually motivated searching for pseudo-words is more effective. [sent-240, score-0.288]
88 This indicates that pseudo-words, through either monolingual searching or synchronous searching, are more effective than words as to being basic translational units. [sent-250, score-0.75]
89 pseudo-word itself as basic translational unit, does not rely very much on higher language model order or longer max phrase length setting. [sent-271, score-0.414]
90 Corpus scale has an influence on computation of sequence significance in long sentences which appear frequently in news domain. [sent-278, score-0.284]
91 Similar to performances on small corpus, wchpwen always performs better than the other two cases, which indicates that Chinese word prefers to have English pseudo-word equivalence which has more than or equal to one word. [sent-281, score-0.332]
92 1 Pseudo-word Unpacking ances on Large Corpus Perform- Table 6 presents pseudo-word unpacking performances on large corpus. [sent-305, score-0.247]
93 It shows that the improvement derives from pseudo-word itself as basic transla- tional unit, does not rely very much on higher language model order or longer max phrase length setting. [sent-313, score-0.242]
94 In fact, slight improvement in pwchpwen and pwchwen is seen after pseudo-word unpacking, which indicates that higher language model order and longer max phrase length impact the performance in these two configurations. [sent-314, score-0.346]
95 4 Conclusion We have presented pseudo-word as a novel machine translational unit for phrase-based machine translation. [sent-328, score-0.22]
96 It is proposed to replace too finegrained word as basic translational unit. [sent-329, score-0.249]
97 Pseudoword is a kind of basic multi-word expression that characterizes minimal sequence of consecutive words in sense of translation. [sent-330, score-0.308]
98 By casting pseudo-word searching problem into a parsing framework, we search for pseudo-words in polynomial time. [sent-331, score-0.274]
99 Experimental results of Chinese-toEnglish translation task show that, in phrasebased machine translation model, pseudo-word performs significantly better than word in both spoken language translation domain and news domain. [sent-332, score-0.382]
100 Removing the power of higher order language model and longer max phrase length, which are inherent in pseudo-words, shows that pseudo-words still improve translational performance significantly over unary words. [sent-333, score-0.416]
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