Phrasal: A Statistical Machine Translation Toolkit for Exploring New Model Features - Cer, Daniel; Galley, Michel; Jurafsky, Daniel; Manning, Christopher D. (Proc. of HLT-NAACL, Demonstration Session, 2010)
We present a new Java-based open source toolkit for phrase-based machine translation. The key innovation provided by the toolkit is to use APIs for integrating new features (/knowledge sources) into the decoding model and for extracting feature statistics from aligned bitexts. The package includes a number of useful features written to these APIs including features for hierarchical reordering, discriminatively trained linear distortion, and syntax based language models. Other useful utilities packaged with the toolkit include: a conditional phrase extraction system that builds a phrase table just for a specific dataset; and an implementation of MERT that allows for pluggable evaluation metrics for both training and evaluation with built in support for a variety of metrics (e.g., TERp, BLEU, METEOR).