This paper describes the MIT-LL/AFRL statistical MT system
and the improvements that were developed during the IWSLT 2013
evaluation campaign. As part of these efforts, we experimented
with a number of extensions to the standard phrase-based model that
improve performance on the Russian to English, Chinese to English,
Arabic to English, and English to French TED-talk translation task.
We also applied our existing ASR system to the TED-talk lecture
ASR task.

We discuss the architecture of the MIT-LL/AFRL MT system,
improvements over our 2012 system, and experiments we ran during 
the IWSLT-2013 evaluation. Specifically, we focus on 1) cross-
entropy filtering of MT training data, and 2) improved optimization
techniques, 3) language modeling, and 4) approximation of out-of-
vocabulary words.