Title: Sub-Phrasal Matching and Structural Templates in Example-Based MT
Example-Based Machine Translation (EBMT) encompasses many different
approaches to data-driven MT. In this work I first look at two different
paradigms of EBMT. I then combine the strengths of these two systems and
build a new engine that combines sub-phrasal matching with structural
templates. The end result is a melding of ideas from EBMT, SMT, and
Xfer. This synthesis results in higher translation quality and more
graceful degradation, yielding 1.5% to 7.5% relative improvement in BLEU
scores.
This work was recently presented at TMI. The full paper can be found
here:
http://dustoftheground.net/
