Presentation on theme: "A Word-Class Approach to Labeling PSCFG Rules for Machine Translation (ACL 2011) Andreas Zollmann and Stephan Vogel Presented by Yun Huang 01/07/2011."— Presentation transcript:
A Word-Class Approach to Labeling PSCFG Rules for Machine Translation (ACL 2011) Andreas Zollmann and Stephan Vogel Presented by Yun Huang 01/07/2011
2 Background PSCFG (Chiang 2005,2007) –Rules: X => (γ/α/ w) X=>( / held talk with Sharon) X=>( X 1 / held talk with X 1 ) X=>( X 1 X 2 / held X 2 with X 1 ) –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=>( X 1 X 2 / held X 2 with X 1 ) –No difference between X 1 and X 2 Maybe we want … –VP=>( PRP NP / held NP with PRP) Idea: multi-label PSCFG. How to label hierarchical phrases?
7 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
9 Experiments Baseline PTB POS Tags Word Class Clustering Phrase Clustering
11 Related Work JHU workshop 2010 –http://www.clsp.jhu.edu/workshops/ws10/grou ps/msgismt/http://www.clsp.jhu.edu/workshops/ws10/grou ps/msgismt/ Other approaches –Phrase clustering –Syntax-augmented MT Source code: –SAMT system
12 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