acl acl2012 acl2012-116 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Wei He ; Hua Wu ; Haifeng Wang ; Ting Liu
Abstract: unkown-abstract
Reference: text
sentIndex sentText sentNum sentScore
1 com Abstract1 We propose a novel approach to improve SMT via paraphrase rules which are automatically extracted from the bilingual training data. [sent-5, score-0.992]
2 Without using extra paraphrase resources, we acquire the rules by comparing the source side of the parallel corpus with the target-to-source translations of the target side. [sent-6, score-1.103]
3 Besides the word and phrase paraphrases, the acquired paraphrase rules mainly cover the structured paraphrases on the sentence level. [sent-7, score-1.399]
4 These rules are employed to enrich the SMT inputs for translation quality improvement. [sent-8, score-0.41]
5 The experimental results show that our proposed approach achieves significant improvements of 1. [sent-9, score-0.029]
6 1 Introduction The translation quality of the SMT system is highly related to the coverage of translation models. [sent-13, score-0.334]
7 However, no matter how much data is used for training, it is still impossible to completely cover the unlimited input sentences. [sent-14, score-0.184]
8 Naturally, a solution to the coverage problem is to bridge the gaps between the input sentences and the translation models, either from the input side, which targets on rewriting the input sentences to the MT-favored expressions, or from This work was done when the first author was visiting Baidu. [sent-16, score-0.661]
9 cn 979 the side of translation models, which tries to enrich the translation models to cover more expressions. [sent-20, score-0.516]
10 In recent years, paraphrasing has been proven useful for improving SMT quality. [sent-21, score-0.103]
11 The proposed methods can be classified into two categories according to the paraphrase targets: (1) enrich translation models to cover more bilingual expressions; (2) paraphrase the input sentences to reduce OOVs or generate multiple inputs. [sent-22, score-1.927]
12 (2008) and Nakov (2008) enriched the SMT models via paraphrasing the training corpora. [sent-25, score-0.073]
13 (2010) and Max (2010) used paraphrases to smooth translation models. [sent-27, score-0.422]
14 For the second category, previous studies mainly focus on finding translations for unknown terms using phrasal paraphrases. [sent-28, score-0.206]
15 (2009) paraphrase unknown terms in the input sentences using phrasal paraphrases extracted from bilingual and monolingual corpora. [sent-31, score-1.34]
16 (2009) rewrite OOVs with entailments and paraphrases acquired from WordNet. [sent-33, score-0.377]
17 (2010) use phrasal paraphrases to build a word lattice to get multiple input candidates. [sent-36, score-0.546]
18 In the above methods, only word or phrasal paraphrases are used for input sentence rewriting. [sent-37, score-0.559]
19 No structured paraphrases on the sentence level have been investigated. [sent-38, score-0.32]
20 However, the information in the sentence level is very important for disambiguation. [sent-39, score-0.076]
21 For example, we can only substitute play with drama in a context related to stage or theatre. [sent-40, score-0.037]
22 Phrasal paraphrase substitutions can hardly solve such kind of problems. [sent-41, score-0.701]
23 In this paper, we propose a method that rewrites Proce dJienjgus, R ofep thueb 5lic0t hof A Knonruea ,l M 8-e1e4ti Jnugly o f2 t0h1e2 A. [sent-42, score-0.028]
24 the input sentences of the SMT system using automatically extracted paraphrase rules which can capture structures on sentence level in addition to paraphrases on the word or phrase level. [sent-45, score-1.396]
25 Without extra paraphrase resources, a novel approach is proposed to acquire paraphrase rules from the bilingual training corpus based on the results of Forward-Translation and Back-Translation. [sent-46, score-1.749]
26 The rules target on rewriting the input sentences to an MT-favored expression to ensure a better translation. [sent-47, score-0.407]
27 The paraphrase rules cover all kinds of paraphrases on the word, phrase and sentence levels, enabling structure reordering, word or phrase insertion, deletion and substitution. [sent-48, score-1.337]
28 The experimental results show that our proposed approach achieves significant improvements of 1. [sent-49, score-0.029]
29 Section 3 introduces our methods that extract paraphrase rules from the bilingual corpus of SMT. [sent-54, score-0.961]
30 Section 4 describes the strategies for constructing word lattice with paraphrase rules. [sent-55, score-0.79]
31 Finally, Section 8 concludes the paper and suggests directions for future work. [sent-58, score-0.03]
32 Back- The Back-Translation method is mainly used for automatic MT evaluation (Rapp 2009). [sent-60, score-0.038]
33 This 980 approach is very helpful when no target language reference is available. [sent-61, score-0.046]
34 The procedure includes translating a text into certain foreign language with the MT system (ForwardTranslation), and translating it back into the original language with the same system (Back- Translation). [sent-63, score-0.138]
35 Finally the translation quality of Back-Translation is evaluated by using the original source texts as references. [sent-64, score-0.183]
36 (2010) reported an interesting phenomenon: given a bilingual text, the BackTranslation results of the target sentences is better than the Forward-Translation results of the source sentences. [sent-66, score-0.197]
37 Clearly, let (S0, T0) be the initial pair of bilingual text. [sent-67, score-0.144]
38 A source-to-target translation system SYS_ST and a target-to-source translation system SYS_TS are trained using the bilingual corpus. [sent-68, score-0.405]
39 is a function of Back-Translation which can be deduced with two rounds of translations: ? [sent-77, score-0.034]
40 In the first round of translation, S0 and T0 are fed into SYS_ST and SYS_TS, and we get T1 and S1 as translation results. [sent-99, score-0.282]
41 In the second round, we translate S1 back into the target side with SYS_ST, and get the translation T2. [sent-100, score-0.275]
42 The procedure is illustrated in Figure 1, which can also formally be described as: 1. [sent-101, score-0.032]
43 (2010) that T2 achieves a higher score than T1 in automatic MT evaluation. [sent-106, score-0.029]
44 This outcome is important because T2 is translated N1342o. [sent-107, score-0.053]
45 Why the machine-generated text results in a better translation than the human-write text? [sent-109, score-0.151]
46 Note that all the texts of S0, S1, S2, T0 and T1 are sentence aligned because the initial parallel corpus (S0, T0) is aligned in the sentence level. [sent-111, score-0.38]
47 The aligned sentence pairs in (S0, S1) can be considered as paraphrases. [sent-112, score-0.155]
48 Taking (S0, S1) as paraphrase resource, we propose a method that automatically extracts paraphrase rules to capture the MT-favored structures. [sent-115, score-1.559]
49 1 Definition of Paraphrase Rules We define a paraphrase rule as follows: 1. [sent-117, score-0.771]
50 A paraphrase rule consists of two parts, lefthand-side (LHS) and right-hand-side (RHS). [sent-118, score-0.771]
51 Both of LHS and RHS consist of nonterminals (slot) and terminals (words). [sent-119, score-0.028]
52 A paraphrase rule in the format of: LHS RHS which means the words matched by LHS can be paraphrased to RHS. [sent-124, score-0.922]
53 2 of paraphrase 981 rules are Selecting Paraphrase Sentence Pairs Following the methods in Section 2, the initial bilingual corpus is (S0, T0). [sent-127, score-1.002]
54 We train a source-totarget PBMT system (SYS_ST) and a target-tosource PBMT system (SYS_TS) on the parallel corpus. [sent-128, score-0.029]
55 As mentioned above, the detailed procedure is: T1 = SYS_ST(S0), S1 = SYS_TS(T0), T2 = SYS_ST(S1). [sent-130, score-0.032]
56 2002) score for every sentence in T2 and T1, using the corresponding sentence in T0 as reference. [sent-132, score-0.152]
57 If the sentence in T2 has a higher BLEU score than the aligned sentence in T1, the corresponding sentences in S0 and S1 are selected as candidate paraphrase sentence pairs, which are used in the following steps of paraphrase extractions. [sent-133, score-1.757]
58 3 Word Alignments Filtering We can construct word alignment between S0 and S1 through T0. [sent-135, score-0.093]
59 On the initial corpus of (S0, T0), we conduct word alignment with Giza++ (Och and Ney, 2000) in both directions and then apply the grow-diag-final heuristic (Koehn et al. [sent-136, score-0.164]
60 Because S1 is generated by feeding T0 into the PBMT system SYS_TS, the word alignment between T0 and S1 can be acquired from the verbose information of the decoder. [sent-138, score-0.222]
61 The word alignments of S0 and S1 contain noises which are produced by either wrong alignment of GIZA++ or translation errors of SYS_TS. [sent-139, score-0.309]
62 To ensure the alignment quality, we use some heuristics to filter the alignment between S0 and S1: 1. [sent-140, score-0.224]
63 If two identical words are aligned in S0 and S1, then remove all the other links to the two words. [sent-141, score-0.079]
64 Stop words (including some function words and punctuations) can only be aligned to either stop words or null. [sent-143, score-0.115]
65 Figure 2 illustrates an example of using the heuristics to filter alignment. [sent-144, score-0.062]
66 4 Extracting Paraphrase Rules From the word-aligned sentence pairs, we then extract a set of rules that are consistent with the word alignments. [sent-146, score-0.259]
67 We use the rule extracting methods of Chiang (2005). [sent-147, score-0.07]
68 However, it is risky to directly replace the input sentence with a paraphrased sentence, since the errors in automatic paraphrase substitution may jeopardize the translation result seriously. [sent-149, score-1.084]
69 To avoid such damage, for a given input sentence, we first transform all paraphrase rules that match the input sentences to phrasal paraphrases, and then build a word lattice 982 LHS:乘坐/ride Rule X1 公共汽车/bus RHS:乘坐/ride X1 巴士/bus 0 1 welcome ride 欢迎 乘坐 乘坐 ride 2 No. [sent-150, score-1.491]
70 10 巴士 bus Figure 3 : Example for Applying Paraphrase Rules for SMT decoder using the phrasal paraphrases. [sent-152, score-0.228]
71 In this case, the decoder can search for the best result among all the possible paths. [sent-153, score-0.03]
72 The input sentences are first segmented into subsentences by punctuations. [sent-154, score-0.13]
73 Then for each subsentence, the matched paraphrase rules are ranked according to: (1) the number of matched words; (2) the frequency of the paraphrase rule in the training data. [sent-155, score-1.783]
74 Actually, the ranking strategy tends to select paraphrase rules that have more matched words (therefore less ambiguity) and higher frequency (therefore more reliable). [sent-156, score-0.935]
wordName wordTfidf (topN-words)
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