Carnegie Mellon University
Browse

Structured Ramp Loss Minimization for Machine Translation

Download (736.02 kB)
journal contribution
posted on 2012-06-01, 00:00 authored by Kevin Gimpel, Noah A. Smith

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 implicit connections to particular forms of ramp loss. We propose to minimize ramp loss directly and present a training algorithm that is easy to implement and that performs comparably to others. Most notably, our structured ramp loss minimization algorithm, RAMPION, is less sensitive to initialization and random seeds than standard approaches.

History

Publisher Statement

Copyright 2012 ACL

Date

2012-06-01

Usage metrics

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC