A Smorgasbord of Features for Statistical Machine Translation

We describe a methodology for rapid experimentation in statistical machine translation which we use to add a large number of features to a baseline system exploiting features from a wide range of levels of syntactic representation. Feature values were combined in a log-linear model to select the highest scoring candidate translation from an n-best list. Feature weights were optimized directly against the BLEU evaluation metric on held-out data. We present results for a small selection of features at each level of syntactic representation.

• Type: Conference
• Year: 2004
• Where: NAACL
• Authors: Franz Josef Och, Daniel Gildea, Sanjeev Khudanpur, Anoop Sarkar, Kenji Yamada, Alexander Fraser, Shankar Kumar, Libin Shen, David Smith, Katherine Eng, Viren Jain, Zhen Jin, Dragomir R. Radev
• Keywords: NAACL 2004, NAACL 2007, Scoring Candidate Translation, Statistical Machine Translation, Syntactic Representation