Statistical XFER: Hybrid Statistical Rule-based Machine Translation Alon Lavie Language Technologies Institute Carnegie Mellon University Joint work with:
AVENUE/LETRAS: Learning-based MT for Languages with Limited Resources Faculty: Jaime Carbonell, Alon Lavie, Lori Levin, Ralf Brown, Robert Frederking Students.
AVENUE/LETRAS: Learning-based MT Approaches for Languages with Limited Resources
The AVENUE Project Data Elicitation System Lori Levin Language Technologies Institute School of Computer Science Carnegie Mellon University.
Computational support for minority languages using a typologically oriented questionnaire system Lori Levin Language Technologies Institute School of Computer.
A Trainable Transfer-based MT Approach for Languages with Limited Resources Alon Lavie Language Technologies Institute Carnegie Mellon University Joint.
Rapid Prototyping of a Transfer-based Hebrew-to-English Machine Translation System
Parallel Reverse Treebanks for the Discovery of Morpho-Syntactic Markings
Resource Acquisition for Syntax-based MT from Parsed Parallel data
