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A Word-Class Approach to Labeling PSCFG Rules for Machine Translation
(ACL 2011)
Andreas Zollmann and Stephan Vogel
Presented by Yun Huang01/07/2011
2
Background
• PSCFG (Chiang 2005,2007)– Rules: X => (γ/α/ w)
• X=>( 和 沙龙 举行 会谈 / held talk with Sharon)
• X=>( 和 X1 举行 会谈 / held talk with X1)
• X=>( 和 X1 举行 X2 / held X2 with X1)
– Glue rules:• S=>(X / X)• S=>(S X / S X)
– Decoding: cube-pruning, etc.
3
Motivation
• Only S and X are used in PSCFG, can not model different rule categories. Example:– X=>( 和 X1 举行 X2 / held X2 with X1)
– No difference between X1 and X2
• Maybe we want …– VP=>( 和 PRP 举行 NP / held NP with PRP)
• Idea: multi-label PSCFG.
• How to label hierarchical phrases?
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• Simple: boundary (POS) tags– I[PRP] saw[VBD] him[PRP]
• Extracted rules:– PRP-PRP => (ich / I)– PRP-PRP => (ihn / him)– VBD-VBD => (gesehen / saw)– VBD-PRP => (habe ihn gesehen / saw him)– VBD-PRP => (Ich habe ihn gesehen / I saw him)– VBD-PRP => (habe PRP-PRP gesehen / saw PRP-P
RP
Labeling from word classes(1/4)
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Labeling from word classes(2/4)
• Accounting for phrase size– 1-word
• PRP=>(Ich | I)• PRP=>(ihn | him)
– 2-word• VBD-PRP => (habe ihn gesehen / saw him)• VBD-PRP => (habe PRP gesehen / saw PRP)
– multiple-word• VBD..PRP => (Ich habe ihn gesehen / I saw him)• VBD..PRP => (Ich habe PRP gesehen / I saw PRP)
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Labeling from word classes(3/4)
• Bilingually tagged corpus– Ich[PRP] habe[AUX] ihn[PRP] gesehen[VBN]– I[PRP] saw[VBD] him[PRP]
• Extracted rules: (“src label+tgt label”)– PRP+PRP => (ich / I)– PRP+PRP => (ihn / him)– VBN+VBD => (gesehen / saw)– AUX..VBN+VBD-PRP => (habe ihn gesehen / saw him)– PRP..VBN+PRP..PRP => (Ich habe ihn gesehen / I saw him)– AUX..VBN+VBD-PRP => (habe PRP+PRP gesehen / saw PRP+
PRP
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Labeling from word classes(4/4)
• Unsupervised word class clustering– MKCLS– Morphological information
• Problems of word classes:– Huge grammar size– Data sparseness– Solution: directly clustering rules
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Clustering phrase pairs
• Directly clustering phrase pairs
• K-means clustering algorithm
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Experiments
Baseline
PTB POS Tags
WordClass Clustering
Phrase Clustering
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Experiments
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Related Work
• JHU workshop 2010– http://www.clsp.jhu.edu/workshops/ws10/grou
ps/msgismt/
• Other approaches– Phrase clustering– Syntax-augmented MT
• Source code:– SAMT system
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Problems
• Too simple, sometimes naïve.– Simple features– Simple clustering method– How to control model complexity
• Future work– Other learning method instead of clustering– Combining hierarchical phrase based model
with syntactical trees