acl acl2013 acl2013-68 knowledge-graph by maker-knowledge-mining

68 acl-2013-Bilingual Data Cleaning for SMT using Graph-based Random Walk


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Author: Lei Cui ; Dongdong Zhang ; Shujie Liu ; Mu Li ; Ming Zhou

Abstract: The quality of bilingual data is a key factor in Statistical Machine Translation (SMT). Low-quality bilingual data tends to produce incorrect translation knowledge and also degrades translation modeling performance. Previous work often used supervised learning methods to filter lowquality data, but a fair amount of human labeled examples are needed which are not easy to obtain. To reduce the reliance on labeled examples, we propose an unsupervised method to clean bilingual data. The method leverages the mutual reinforcement between the sentence pairs and the extracted phrase pairs, based on the observation that better sentence pairs often lead to better phrase extraction and vice versa. End-to-end experiments show that the proposed method substantially improves the performance in largescale Chinese-to-English translation tasks.

Reference: text


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 com Abstract The quality of bilingual data is a key factor in Statistical Machine Translation (SMT). [sent-4, score-0.261]

2 Low-quality bilingual data tends to produce incorrect translation knowledge and also degrades translation modeling performance. [sent-5, score-0.646]

3 Previous work often used supervised learning methods to filter lowquality data, but a fair amount of human labeled examples are needed which are not easy to obtain. [sent-6, score-0.053]

4 To reduce the reliance on labeled examples, we propose an unsupervised method to clean bilingual data. [sent-7, score-0.318]

5 The method leverages the mutual reinforcement between the sentence pairs and the extracted phrase pairs, based on the observation that better sentence pairs often lead to better phrase extraction and vice versa. [sent-8, score-0.96]

6 End-to-end experiments show that the proposed method substantially improves the performance in largescale Chinese-to-English translation tasks. [sent-9, score-0.153]

7 1 Introduction Statistical machine translation (SMT) depends on the amount of bilingual data and its quality. [sent-10, score-0.414]

8 In real-world SMT systems, bilingual data is often mined from the web where low-quality data is inevitable. [sent-11, score-0.296]

9 The low-quality bilingual data degrades the quality of word alignment and leads to the incorrect phrase pairs, which will hurt the translation performance of phrase-based SMT systems (Koehn et al. [sent-12, score-0.723]

10 Therefore, it is very important to exploit data quality information to improve the translation modeling. [sent-14, score-0.153]

11 Previous work on bilingual data cleaning often involves some supervised learning methods. [sent-15, score-0.393]

12 Several bilingual data mining systems (Resnik and ∗This work has been done while the first author ing Microsoft Research Asia. [sent-16, score-0.261]

13 Maximum entropy or SVM based classifiers are built to filter some non-parallel data or partial-parallel data. [sent-20, score-0.053]

14 Although these methods can filter some low-quality bilingual data, they need sufficient human labeled training instances to build the model, which may not be easy to acquire. [sent-21, score-0.314]

15 To this end, we propose an unsupervised approach to clean the bilingual data. [sent-22, score-0.318]

16 It is intuitive that high-quality parallel data tends to produce better phrase pairs than low-quality data. [sent-23, score-0.39]

17 Meanwhile, it is also observed that the phrase pairs that appear frequently in the bilingual corpus are more reliable than less frequent ones because they are more reusable, hence most good sentence pairs are prone to contain more frequent phrase pairs (Foster et al. [sent-24, score-1.068]

18 This kind of mutual reinforcement fits well into the framework of graph-based random walk. [sent-27, score-0.281]

19 When a phrase pair p is extracted from a sentence pair s, s is considered casting a vote for p. [sent-28, score-0.447]

20 The higher the number of votes a phrase pair has, the more reliable of the phrase pair. [sent-29, score-0.518]

21 Similarly, the quality of the sentence pair s is determined by the number of votes casted by the extracted phrase pairs from s. [sent-30, score-0.522]

22 In this paper, a PageRank-style random walk algorithm (Brin and Page, 1998; Mihalcea and Tarau, 2004; Wan et al. [sent-31, score-0.221]

23 , 2007) is conducted to iteratively compute the importance score of each sentence pair that indicates its quality: the higher the better. [sent-32, score-0.318]

24 Unlike other data filtering methods, our proposed method utilizes the importance scores of sentence pairs as fractional counts to calculate the phrase translation probabilities based on Maximum Likelihood Estimation (MLE), thereby none of the bilingual data is filtered out. [sent-33, score-1.225]

25 Experimental results show that our proposed approach substantially improves the performance in large-scale Chinese-to-English translation tasks. [sent-34, score-0.153]

26 1 Graph-based random walk Graph-based random walk is a general algorithm to approximate the importance of a vertex within the graph in a global view. [sent-38, score-0.794]

27 In our method, the vertices denote the sentence pairs and phrase pairs. [sent-39, score-0.503]

28 The importance of each vertex is propagated to other vertices along the edges. [sent-40, score-0.45]

29 Depending on different scenarios, the graph can take directed or undirected, weighted or un-weighted forms. [sent-41, score-0.057]

30 Starting from the initial scores assigned in the graph, the algorithm is applied to recursively compute the importance scores of vertices until it converges, or the difference between two consecutive iterations falls below a pre-defined threshold. [sent-42, score-0.421]

31 2 Graph construction Given the sentence pairs that are word-aligned automatically, an undirected, weighted bipartite graph is constructed which maps the sentence pairs and the extracted phrase pairs to the vertices. [sent-44, score-0.791]

32 An edge between a sentence pair vertex and a phrase pair vertex is added if the phrase pair can be extracted from the sentence pair. [sent-45, score-1.2]

33 Mutual reinforcement scores are defined on edges, through which the importance scores are propagated between vertices. [sent-46, score-0.389]

34 Formally, the bipartite graph is defined as: G = (V, E) where V = S ∪ P is the vertex set, S = {si |1 ≤ wi ≤ n} i=s tShe ∪ set osf t aell v esretnetxen sceet, pairs. [sent-48, score-0.276]

35 P|1 = {pj | ≤1 ≤ j ≤ m} oisf t ahlel sseetn eofn caell phrase pairs {wphi|c1h are jex ≤tra mcte}d sfr tohme sSe t b oafse adll on tahsee w paoirrds alignment. [sent-49, score-0.306]

36 E is the edge set in which the edges are between S and P, thereby E = {hsi, pji |si ∈ S, pj ∈ P, φ(si, pj) = 1}. [sent-50, score-0.71]

37 φ(si,pj) =(01 iofth pejrcwainse be extracted from si 2. [sent-51, score-0.259]

38 where PF(si, pj) is the phrase pair frequency in a sentence pair and IPF(pj) is the inverse phrase pair frequency of pj in the whole bilingual corpus. [sent-54, score-1.418]

39 , 2007), we compute the importance scores of sentence pairs and phrase pairs using a PageRank-style algorithm. [sent-57, score-0.677]

40 Let u(si) and v(pj) denote the scores of a sentence pair vertex and a phrase pair vertex. [sent-59, score-0.69]

41 85 that is same as the original PageRank, N(si) = {j | hsi, pji ∈ E}, M(pj) = {i| hsi, pji ∈ E}. [sent-61, score-0.288]

42 Algorithm 1 iteratively updates the scores of sentence pairs and phrase pairs (lines 10-26). [sent-64, score-0.599]

43 The computation ends when difference between two consecutive iterations is lower than a pre-defined threshold δ (10−12 in this study). [sent-65, score-0.066]

44 4 Parallelization When the random walk runs on some large bilin- gual corpora, even filtering phrase pairs that appear only once would still require several days of 1 CPU time for a number of iterations. [sent-67, score-0.608]

45 for al)l j← ←∈ 0 N(si) do − F(si) ← F(si) + Pk∈Mri(jpj)rkj · v(pj)(n−1) end for u(si) (n) ← (1 − d) + d · F(si) end for for all j ∈ {0 . [sent-84, score-0.072]

46 Before the iterative computation starts, the sum of the outlink weights for each vertex is computed first. [sent-90, score-0.226]

47 The edges are randomly partitioned into sets of roughly equal size. [sent-91, score-0.073]

48 Each edge hsi, pji can generate two key-value pairs eind gthee h sformait hsi, riji raantde hpj , riji . [sent-92, score-0.507]

49 aTluhee pairs iwnit hth eth feo same key are asnudmm hped locally and accumulated across different machines. [sent-93, score-0.115]

50 Then, in each iteration, the score of each vertex is updated according to the sum of the normalized inlink weights. [sent-94, score-0.181]

51 The key-value pairs are gener- ated in the format hsi, Pk∈Mri(jpj)rkj · v(pj)i and hpj, Pk∈Nri(jsi)rik · u(si)iP. [sent-95, score-0.115]

52 These key-value pairs are aPlso randomly partitioned and summed across different machines. [sent-96, score-0.154]

53 Since long sentence pairs usually extract more phrase pairs, we need to normalize the importance scores based on the sentence length. [sent-97, score-0.642]

54 5 Integration into translation modeling After sufficient number of iterations, the importance scores of sentence pairs (i. [sent-100, score-0.524]

55 Instead of simple filtering, we use the scores of sentence pairs as the fractional counts to re-estimate the translation probabilities of phrase pairs. [sent-103, score-0.7]

56 Given a phrase pair p = hf¯, ei, A(f¯) and B(¯ e) i Gndiivceante a t pheh sets poafi rse pnt =en hcefs, ethi,at A f(¯ and e appear. [sent-104, score-0.279]

57 Then the translation probability is defined as: PCW(f¯| e¯) =Pi∈PA(jf∈¯)B∩B(¯ e() e¯)u(us(js)i) × × c cj(i( e¯f)¯, e¯ ) where ci (·) denotesP the count of the phrase or phrase pair i dne si. [sent-105, score-0.623]

58 sPC thWe( cf¯o| e¯u)n tan odf PthCeW p(¯h er|fa¯s)e are named as Corpus Weighting (CW) bPase(d¯ e |translation probability, which are integrated into the loglinear model in addition to the conventional phrase translation probabilities (Koehn et al. [sent-106, score-0.379]

59 1 Setup We evaluated our bilingual data cleaning approach on large-scale Chinese-to-English machine translation tasks. [sent-109, score-0.546]

60 The bilingual data we used was mainly mined from the web (Jiang et al. [sent-110, score-0.296]

61 , 2009)1 , as well as the United Nations parallel corpus released by LDC and the parallel corpus released by China Workshop on Machine Translation (CWMT), which contain around 30 million sentence pairs in total after removing duplicated ones. [sent-111, score-0.429]

62 A phrase-based decoder was implemented based on inversion transduction grammar (Wu, 1997). [sent-114, score-0.125]

63 The performance of this decoder is similar to the state-of-the-art phrase-based decoder in Moses, but the implementation is more straightforward. [sent-115, score-0.076]

64 We use the following feature functions in the log-linear model: 1Although supervised data cleaning has been done in the post-processing, the corpus still contains a fair amount of noisy data based on our random sampling. [sent-116, score-0.266]

65 216 8094 Table 2: BLEU(%) of Chinese-to-English translation tasks on multiple testing datasets (p ”-numberM” < 0. [sent-127, score-0.153]

66 05), where denotes we simply filter number million low scored sentence pairs from the bilingual data and use others to extract the phrase table. [sent-128, score-0.7]

67 ”CW” means the corpus weighting feature, which incorporates sentence scores from random walk as fractional counts to re-estimate the phrase translation probabilities. [sent-129, score-0.771]

68 • • phrase translation probabilities and lexical weights rina nbsolathti doinre pctroiobnasb (4 features); 5-gram language model with Kneser-Ney smoothing (1 feature); • lexicalized reordering model (1 feature); • phrase count and word count (2 features). [sent-130, score-0.644]

69 The translation model was trained over the word-aligned bilingual corpus conducted by GIZA++ (Och and Ney, 2003) in both directions, and the diag-grow-final heuristic was used to refine the symmetric word alignment. [sent-131, score-0.414]

70 , 2006) was trained over the 40% randomly sampled sentence pairs from our parallel data. [sent-135, score-0.279]

71 In the baseline system, the phrase pairs that appear only once in the bilingual data are simply discarded because most of them are noisy. [sent-142, score-0.567]

72 The results show weijing tansuo de xin lingyu 未经 探索 的 新 领域 未经 探索 的 新 领域 uncharted waters unexplored new areas Figure 2: The left one is the non-literal translation in our bilingual corpus. [sent-148, score-0.549]

73 The right one is the literal translation made by human for comparison. [sent-149, score-0.194]

74 that the ”leaving-one-out” method performs almost the same as our baseline, thereby cannot bring other benefits to the system. [sent-150, score-0.069]

75 3 Results We evaluate the proposed bilingual data cleaning method by incorporating sentence scores into translation modeling. [sent-152, score-0.688]

76 In addition, we also compare with several settings that filtering low-quality sentence pairs from the bilingual data based on the importance scores. [sent-153, score-0.651]

77 5M, 1M } sentence pairs are sfitlt eNred = b {ef o0r. [sent-156, score-0.195]

78 Although ftihltee simple r beil tihnegual data filtering can improve the performance on some datasets, it is difficult to determine the bor- der line and translation performance is fluctuated. [sent-158, score-0.234]

79 One main reason is in the proposed random walk approach, the bilingual sentence pairs with nonliteral translations may get lower scores because they appear less frequently compared with those literal translations. [sent-159, score-0.78]

80 Crudely filtering out these data may degrade the translation performance. [sent-160, score-0.275]

81 For example, we have a sentence pair in the bilingual corpus shown in the left part of Figure 2. [sent-161, score-0.429]

82 Although the translation is correct in this situation, translating the Chinese word ”lingyu” to ”waters” appears very few times since the common translations are ”areas” or ”fields”. [sent-162, score-0.153]

83 However, simply filtering out this kind of sentence pairs may lead to some loss of native English expressions, thereby the trans343 lation performance is unstable since both nonparallel sentence pairs and non-literal but parallel sentence pairs are filtered. [sent-163, score-0.819]

84 Therefore, we use the importance score of each sentence pair to estimate the phrase translation probabilities. [sent-164, score-0.626]

85 It consistently brings substantial improvements compared to the baseline, which demonstrates graph-based random walk indeed improves the translation modeling performance for our SMT system. [sent-165, score-0.374]

86 , 2012), they evaluated phrasebased SMT systems trained on parallel data with different proportions of synthetic noisy data. [sent-168, score-0.161]

87 They suggested that when collecting larger, noisy parallel data for training phrase-based SMT, cleaning up by trying to detect and remove incorrect alignments can actually degrade performance. [sent-169, score-0.366]

88 Based on our method, sometimes filtering noisy data leads to unexpected results. [sent-171, score-0.158]

89 The reason is two-fold: on the one hand, the non-literal parallel data makes false positive in noisy data detection; on the other hand, large-scale SMT systems is relatively robust and tolerant to noisy data, especially when we remove frequency1phrase pairs. [sent-172, score-0.238]

90 Therefore, we propose to integrate the importance scores when re-estimating phrase pair probabilities in this paper. [sent-173, score-0.49]

91 The importance scores can be considered as a kind of contribution constraint, thereby high-quality parallel data contributes more while noisy parallel data contributes less. [sent-174, score-0.556]

92 4 Conclusion and Future Work In this paper, we develop an effective approach to clean the bilingual data using graph-based random walk. [sent-175, score-0.375]

93 For future work, we will extend our method to explore the relationships of sentence-to-sentence and phrase-to-phrase, which is beyond the existing sentence-to-phrase mutual reinforcement. [sent-177, score-0.075]

94 The impact of sentence alignment errors on phrase-based machine translation performance. [sent-195, score-0.272]

95 Mining bilingual data from the web with adaptively learnt patterns. [sent-200, score-0.261]

96 A sys- tematic comparison of various statistical alignment models. [sent-224, score-0.039]

97 A dom tree alignment model for mining parallel data from the web. [sent-246, score-0.123]

98 Towards an iterative reinforcement approach for simultaneous document summarization and keyword extraction. [sent-251, score-0.158]

99 Stochastic inversion transduction grammars and bilingual parsing of parallel corpora. [sent-256, score-0.432]

100 Maximum entropy based phrase reordering model for statistical machine translation. [sent-265, score-0.229]


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