A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is
Lecture Notes in Computer Science
We argue that for purely incremental unsupervised learning of phrasal inversion transduction grammars, a minimum description length driven, iterative top-down rule segmentation approach that is the polar opposite of Saers, Addanki, and Wu's previous 2012 bottom-up iterative rule chunking model yields significantly better translation accuracy and grammar parsimony. We still aim for unsupervised bilingual grammar induction such that training and testing are optimized upon the same exactdoi:10.1007/978-3-642-39593-2_20 fatcat:bpywavv47bes3fx22zfxisrp2a