New Confidence Measures for Statistical Machine Translation
Computation and Language
2009-02-09 v1
Abstract
A confidence measure is able to estimate the reliability of an hypothesis provided by a machine translation system. The problem of confidence measure can be seen as a process of testing : we want to decide whether the most probable sequence of words provided by the machine translation system is correct or not. In the following we describe several original word-level confidence measures for machine translation, based on mutual information, n-gram language model and lexical features language model. We evaluate how well they perform individually or together, and show that using a combination of confidence measures based on mutual information yields a classification error rate as low as 25.1% with an F-measure of 0.708.
Cite
@article{arxiv.0902.1033,
title = {New Confidence Measures for Statistical Machine Translation},
author = {Sylvain Raybaud and Caroline Lavecchia and David Langlois and Kamel Smaïli},
journal= {arXiv preprint arXiv:0902.1033},
year = {2009}
}