acl acl2012 acl2012-108 knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Yang Feng ; Dongdong Zhang ; Mu Li ; Qun Liu
Abstract: We present a hierarchical chunk-to-string translation model, which can be seen as a compromise between the hierarchical phrasebased model and the tree-to-string model, to combine the merits of the two models. With the help of shallow parsing, our model learns rules consisting of words and chunks and meanwhile introduce syntax cohesion. Under the weighed synchronous context-free grammar defined by these rules, our model searches for the best translation derivation and yields target translation simultaneously. Our experiments show that our model significantly outperforms the hierarchical phrasebased model and the tree-to-string model on English-Chinese Translation tasks.
Reference: text
sentIndex sentText sentNum sentScore
1 Hierarchical Chunk-to-String Translation∗ Yang Feng† Dongdong Zhang‡ Mu Li‡ Ming Zhou‡ Qun Liu⋆ † Department of Computer Science ‡ Microsoft Research Asia University of Sheffield do zhang@mi cro s o ft . [sent-1, score-0.169]
2 cn ct Abstract We present a hierarchical chunk-to-string translation model, which can be seen as a compromise between the hierarchical phrasebased model and the tree-to-string model, to combine the merits of the two models. [sent-9, score-1.018]
3 With the help of shallow parsing, our model learns rules consisting of words and chunks and meanwhile introduce syntax cohesion. [sent-10, score-0.592]
4 Under the weighed synchronous context-free grammar defined by these rules, our model searches for the best translation derivation and yields target translation simultaneously. [sent-11, score-0.703]
5 Our experiments show that our model significantly outperforms the hierarchical phrasebased model and the tree-to-string model on English-Chinese Translation tasks. [sent-12, score-0.548]
6 ∗This work was done when the first author visited Microsoft Research Asia as an intern. [sent-15, score-0.045]
7 950 However, it is often desirable to consider syntactic constituents of subphrases, e. [sent-16, score-0.134]
8 the hierarchical phrase X → hX1 for X2, X2 de X1i can be applied to both of the following strings in Figure 1 “A request for a purchase of shares” “filed for bankruptcy”, and get the following translation, respectively “goumai gufen de shenqing” “pochan de shenqing”. [sent-18, score-0.5]
9 In the former, “A request” is a NP and this rule acts correctly while in the latter “filed” is a VP and this rule gives a wrong reordering. [sent-19, score-0.242]
10 If we specify the first X on the right-hand side to NP, this kind of errors can be avoided. [sent-20, score-0.112]
11 , 2006) introduces linguistic syntax via source parse to direct word reordering, especially longdistance reordering. [sent-23, score-0.224]
12 Furthermore, this model is formalised as Tree Substitution Grammars, so it observes syntactic cohesion. [sent-24, score-0.177]
13 Syntactic cohesion means that the translation of a string covered by a subtree in a source parse tends to be continuous. [sent-25, score-0.426]
14 Fox (2002) shows that translation between English and French satisfies cohesion in the majority cases. [sent-26, score-0.304]
15 Many previous works show promising results with an assumption that syntactic cohesion explains almost all translation movement for some language pairs (Wu, 1997; Yamada and Knight, 2001 ; Eisner, 2003; Graehl and Knight, 2004; Quirk et al. [sent-27, score-0.354]
16 This will lead to data sparseness and being vulnerable to parse errors. [sent-33, score-0.193]
17 In this paper, we present a hierarchical chunk-tostring translation model to combine the merits of the two models. [sent-34, score-0.602]
18 Instead of parse trees, our model introduces linguistic information in the form of chunks, so it does not need to care the internal structures and the roles in the main sentence of chunks. [sent-35, score-0.311]
19 Based on shallow parsing results, it learns rules consisting of either words (terminals) or chunks (nonterminals), where adjacent chunks are packed into one nonterminal. [sent-36, score-0.842]
20 It searches for the best derivation through the SCFG-motivated space defined by these rules and get target translation simultaneously. [sent-37, score-0.515]
21 In some sense, our model can be seen as a compromise between the hierarchical phrase-based model and the tree-to- string model, specifically • • • Compared with the hierarchical phrase-based model, eitd integrates linguistic syntax saen-db sseatdisfies syntactic cohesion. [sent-38, score-0.913]
22 Compared with the tree-to-string model, it only nCeoemdsp to perform seh tarleleo-wto parsing wodhieclh, i itn otnrolyduces less parsing errors. [sent-39, score-0.16]
23 Besides, our model allows a nonterminal in a rule to cover several chunks, which can alleviate data sparseness and the influence of parsing errors. [sent-40, score-0.494]
24 we refine our hierarchical chunk-to-string mwoede rle ifnintoe two rmo hdieerlsa:r a liocaolse model (Section 2. [sent-41, score-0.438]
25 1) which is more similar to the hierarchical phrase-based model and a tight model (Section 2. [sent-42, score-0.588]
26 The experiments show that on the 2008 NIST English-Chinese MT translation test set, both the loose model and the tight model outperform the hierarchical phrase-based model and the tree-to-string model, where the loose model has a better perfor- mance. [sent-44, score-1.251]
27 While in terms of speed, the tight model runs faster and its speed ranking is between the treeto-string model and the hierarchical phrase-based model. [sent-45, score-0.627]
28 951 NP IN NP IN NP VBD VP Agouremqauiestgfuofrenapduerchaseshenoqfingsharesbeiwasdimjia doe Tghaei bankyinhanghasyijnfgiledshenfqoirng banpokcruhapntcy 购买 NP 该 的 股份 VBZ 银行 申请 被 递交 (a) VBN 已经 (b) IN NP 申请 破产 Figure 1: A running example of two sentences. [sent-46, score-0.048]
29 For each sentence, the first row gives the chunk sequence. [sent-47, score-0.346]
30 S NP VP DT NN VBZ VP The bank has VBN PP filed IN NP for NN bankruptcy (a) A parse B-NP The I-NP bank B-VBZ has (b) A chunk tree B-VBN filed sequence got B-IN for B-NP bankruptcy from the parse tree Figure 2: An example of shallow parsing. [sent-48, score-1.689]
31 2 Modeling Shallow parsing (also chunking) is an analysis of a sentence which identifies the constituents (noun groups, verbs, verb groups, etc), but neither specifies their internal structures, nor their roles in the main sentence. [sent-49, score-0.28]
32 In Figure 1, we give the chunk sequence in the first row for each sentence. [sent-50, score-0.388]
33 We treat shallow parsing as a sequence label task, and a sentence f can have many possible different chunk la- bel sequences. [sent-51, score-0.604]
34 A SCFG produces a derivation by starting with a pair of start symbols and recursively rewrites every two coindexed nonterminals with the corresponding components of a matched rule. [sent-54, score-0.391]
35 A derivation yields a pair of strings on the right-hand side which are translation of each other. [sent-55, score-0.472]
36 In a weighted SCFG, each rule has a weight and the total weight of a derivation is the production of the weights of the rules used by the derivation. [sent-56, score-0.394]
37 A translation may be produced by many different derivations and we only use the best derivation to evaluate its probability. [sent-57, score-0.341]
38 We further refine our hierarchical chunk-to-string model into two models: a loose model which is more similar to the hierarchical phrase-based model and a tight model which is more similar to the tree-tostring model. [sent-59, score-1.221]
39 The two models differ in the form of rules and the way of estimating rule probabilities. [sent-60, score-0.171]
40 While for decoding, we employ the same decoding algorithm for the two models: given a test sentence, the decoders first perform shallow parsing to get the best chunk sequence, then apply a CYK parsing algorithm with beam search. [sent-61, score-0.688]
41 1 A Loose Model In our model, we employ rules containing nonterminals to handle long-distance reordering where boundary words play an important role. [sent-63, score-0.363]
42 So for the subphrases which cover more than one chunk, we just maintain boundary chunks: we bundle adjacent chunks into one nonterminal and denote it as the first chunk tag immediately followed by “-” and next followed by the last chunk tag. [sent-64, score-1.404]
wordName wordTfidf (topN-words)
[('chunk', 0.3), ('hierarchical', 0.261), ('filed', 0.241), ('subphrases', 0.241), ('chunks', 0.221), ('np', 0.188), ('tight', 0.179), ('translation', 0.177), ('loose', 0.169), ('derivation', 0.164), ('bankruptcy', 0.158), ('scfg', 0.154), ('nonterminals', 0.143), ('shallow', 0.129), ('cohesion', 0.127), ('sheffield', 0.121), ('shenqing', 0.121), ('nonterminal', 0.116), ('vp', 0.109), ('rule', 0.1), ('cro', 0.095), ('merits', 0.09), ('compromise', 0.09), ('vbn', 0.09), ('vbz', 0.085), ('constituents', 0.084), ('terminals', 0.08), ('feng', 0.08), ('parsing', 0.08), ('parse', 0.077), ('model', 0.074), ('ft', 0.074), ('request', 0.074), ('roles', 0.072), ('rules', 0.071), ('denoting', 0.069), ('mi', 0.067), ('side', 0.066), ('phrasebased', 0.065), ('strings', 0.065), ('sparseness', 0.063), ('got', 0.062), ('cover', 0.061), ('bank', 0.059), ('production', 0.059), ('syntax', 0.058), ('searches', 0.056), ('refine', 0.055), ('synchronous', 0.055), ('asia', 0.054), ('vulnerable', 0.053), ('iuqun', 0.053), ('bel', 0.053), ('khk', 0.053), ('observes', 0.053), ('purchase', 0.053), ('reordering', 0.052), ('employ', 0.052), ('syntactic', 0.05), ('rle', 0.048), ('aln', 0.048), ('doe', 0.048), ('microsoft', 0.048), ('get', 0.047), ('row', 0.046), ('specify', 0.046), ('boundary', 0.045), ('longdistance', 0.045), ('conveniently', 0.045), ('hk', 0.045), ('kd', 0.045), ('mul', 0.045), ('sin', 0.045), ('visited', 0.045), ('string', 0.045), ('knight', 0.044), ('introduces', 0.044), ('internal', 0.044), ('matched', 0.044), ('tree', 0.043), ('besides', 0.043), ('nn', 0.043), ('adjacent', 0.042), ('vbd', 0.042), ('acts', 0.042), ('dongdong', 0.042), ('xc', 0.042), ('mingzhou', 0.042), ('reorderings', 0.042), ('grammars', 0.042), ('sequence', 0.042), ('rewrites', 0.04), ('followed', 0.039), ('learns', 0.039), ('speed', 0.039), ('groups', 0.039), ('occurrences', 0.039), ('fox', 0.039), ('etc', 0.039), ('packed', 0.039)]
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