62 citations · 102 across the 3 of their papers we have counts for
3 papers
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation
Michael Bloodgood, Chris Callison-Burch
We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck t…
Using Mechanical Turk to Build Machine Translation Evaluation Sets
Michael Bloodgood, Chris Callison-Burch
Building machine translation (MT) test sets is a relatively expensive task. As MT becomes increasingly desired for more and more language pairs and more and more domains, it become…
Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach
Kathryn Baker, Michael Bloodgood, Chris Callison-Burch +5
We describe a unified and coherent syntactic framework for supporting a semantically-informed syntactic approach to statistical machine translation. Semantically enriched syntactic…