Paper: Structured Ramp Loss Minimization for Machine Translation

ACL ID N12-1023
Title Structured Ramp Loss Minimization for Machine Translation
Venue Annual Conference of the North American Chapter of the Association for Computational Linguistics
Session Main Conference
Year 2012

This paper seeks to close the gap between training algorithms used in statistical machine translation and machine learning, specifically the framework of empirical risk minimization. We review well-known algorithms, arguing that they do not optimize the loss functions they are assumed to optimize when applied to machine translation. Instead, most have im- plicit connections to particular forms of ramp loss. We propose to minimize ramp loss di- rectly and present a training algorithm that is easy to implement and that performs compa- rably to others. Most notably, our structured ramp loss minimization algorithm, RAMPION, is less sensitive to initialization and random seeds than standard approaches.