acl acl2013 acl2013-180 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Feifei Zhai ; Jiajun Zhang ; Yu Zhou ; Chengqing Zong
Abstract: Predicate-argument structure (PAS) has been demonstrated to be very effective in improving SMT performance. However, since a sourceside PAS might correspond to multiple different target-side PASs, there usually exist many PAS ambiguities during translation. In this paper, we group PAS ambiguities into two types: role ambiguity and gap ambiguity. Then we propose two novel methods to handle the two PAS ambiguities for SMT accordingly: 1) inside context integration; 2) a novel maximum entropy PAS disambiguation (MEPD) model. In this way, we incorporate rich context information of PAS for disambiguation. Then we integrate the two methods into a PASbased translation framework. Experiments show that our approach helps to achieve significant improvements on translation quality. 1
Reference: text
sentIndex sentText sentNum sentScore
1 cn Abstract Predicate-argument structure (PAS) has been demonstrated to be very effective in improving SMT performance. [sent-4, score-0.022]
2 However, since a sourceside PAS might correspond to multiple different target-side PASs, there usually exist many PAS ambiguities during translation. [sent-5, score-0.258]
3 In this paper, we group PAS ambiguities into two types: role ambiguity and gap ambiguity. [sent-6, score-0.395]
4 Then we propose two novel methods to handle the two PAS ambiguities for SMT accordingly: 1) inside context integration; 2) a novel maximum entropy PAS disambiguation (MEPD) model. [sent-7, score-0.384]
5 In this way, we incorporate rich context information of PAS for disambiguation. [sent-8, score-0.076]
6 Then we integrate the two methods into a PASbased translation framework. [sent-9, score-0.089]
7 Experiments show that our approach helps to achieve significant improvements on translation quality. [sent-10, score-0.066]
8 1 Introduction Predicate-argument structure (PAS) depicts the relationship between a predicate and its associated arguments, which indicates the skeleton structure of a sentence on semantic level. [sent-11, score-0.133]
9 Basically, PAS agrees much better between two languages than syntax structure (Fung et al. [sent-12, score-0.047]
10 Considering that current syntaxbased translation models are always impaired by cross-lingual structure divergence (Eisner, 2003; Zhang et al. [sent-14, score-0.166]
11 , 2010), PAS is really a better representation of a sentence pair to model the bilingual structure mapping. [sent-15, score-0.07]
12 However, since a source-side PAS might correspond to multiple different target-side PASs, there usually exist many PAS ambiguities during translation. [sent-16, score-0.229]
13 For example, in Figure 1, (a) and (b) carry the same source-side PAS <[A0]1 [Pred(是)]2 [A1]3> for Chinese predicate “是”. [sent-17, score-0.044]
14 However, in Figure 1(a), the corresponding target-side-like PAS is <[X1] [X2] [X3]>, while in Figure 1(b), the counterpart target-side-like PAS 1 is <[X2] [X3] [X1]>. [sent-18, score-0.022]
15 This is because the two PASs play different roles in their corresponding sentences. [sent-19, score-0.026]
16 Actually, Figure 1(a) is an independ- ent PAS, while Figure 1(b) is a modifier of the noun phrase “中 国 和 俄罗斯”. [sent-20, score-0.036]
17 We call this kind of PAS ambiguity role ambiguity. [sent-21, score-0.169]
18 Meanwhile, Figure 1 also depicts another kind of PAS ambiguity. [sent-24, score-0.065]
19 However, they are different because in Figure 1(c), there is a gap string “对 运动员” between [A0] and [Pred]. [sent-26, score-0.139]
20 Generally, the gap strings are due to the low recall of automatic semantic role labeling (SRL) or complex sentence structures. [sent-27, score-0.185]
21 For example, in Figure 1(c), the gap string “对 运动员 ” is actually an argument “AM-PRP” of the PAS, but the SRL system has 1We use target-side-like PAS to refer to a list of general non-terminals in target language order, where a nonterminal aligns to a source argument. [sent-28, score-0.203]
22 1127 Proce dingsS o f ita h,e B 5u1lgsta Arinan,u Aaulg Musete 4ti-n9g 2 o0f1 t3h. [sent-29, score-0.013]
23 Ac s2s0o1ci3a Atiosnso fcoirat Cio nm foprut Caotimonpaulta Lti nognuails Lti cnsg,u piasgteics 1 27–1 36, ignored it. [sent-31, score-0.03]
24 We call this kind of PAS ambiguity gap ambiguity. [sent-32, score-0.252]
25 During translation, these PAS ambiguities will greatly affect the PAS-based translation models. [sent-33, score-0.244]
26 Therefore, in order to incorporate the bilingual PAS into machine translation effectively, we need to decide which target-side-like PAS should be chosen for a specific source-side PAS. [sent-34, score-0.126]
27 In this paper, we propose two novel methods to incorporate rich context information to handle PAS ambiguities. [sent-36, score-0.132]
28 Towards the gap ambiguity, we adopt a method called inside context integration to extend PAS to IC-PAS. [sent-37, score-0.237]
29 In terms of IC-PAS, the gap strings are combined effectively to deal with the gap ambiguities. [sent-38, score-0.29]
30 As to the role ambiguity, we design a novel maximum entropy PAS disambiguation (MEPD) model to combine various context features, such as context words of PAS. [sent-39, score-0.2]
31 For each ambiguous source-side PAS, we build a specific MEPD model to select appropriate target-side-like PAS for translation. [sent-40, score-0.024]
32 We will detail the two methods in Section 3 and 4 respectively. [sent-41, score-0.012]
33 Finally, we integrate the above two methods into a PAS-based translation framework (Zhai et al. [sent-42, score-0.105]
34 Experiments show that the two PAS disambiguation methods significantly improve the baseline translation system. [sent-44, score-0.108]
35 The main contribution of this work can be concluded as follows: 1) We define two kinds of PAS ambiguities: role ambiguity and gap ambiguity. [sent-45, score-0.256]
36 To our best knowledge, we are the first to handle these PAS ambiguities for SMT. [sent-46, score-0.193]
37 2) Towards the two different ambiguities, we design two specific methods for PAS disambiguation: inside context integration and the novel MEPD model. [sent-47, score-0.163]
38 2 PAS-based Translation Framework PAS-based translation framework is to perform translation based on PAS transformation (Zhai et al. [sent-48, score-0.21]
39 In the framework, a source-side PAS is first converted into target-side-like PASs by PAS transformation rules, and then perform translation based on the obtained target-side-like PASs. [sent-50, score-0.141]
40 1 PAS Transformation Rules PAS transformation rules (PASTR) are used to convert a source-side PAS into a target one. [sent-52, score-0.095]
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