The bionic DBMS is coming, but what will it look like?
Ryan Johnson, Ippokratis Pandis
CIDR 2013
We report an empirical study of n-gram posterior probability confidence measures for statistical machine translation (SMT). We first describe an efficient and practical algorithm for rapidly computing n-gram posterior probabilities from large translation word lattices. These probabilities are shown to be a good predictor of whether or not the n-gram is found in human reference translations, motivating their use as a confidence measure for SMT. Comprehensive n-gram precision and word coverage measurements are presented for a variety of different language pairs, domains and conditions. We analyze the effect on reference precision of using single or multiple references, and compare the precision of posteriors computed from k-best lists to those computed over the full evidence space of the lattice. We also demonstrate improved confidence by combining multiple lattices in a multi-source translation framework. © 2012 The Author(s).
Ryan Johnson, Ippokratis Pandis
CIDR 2013
Shashank Ahire, Melissa Guyre, et al.
CUI 2025
Leonid Karlinsky, Joseph Shtok, et al.
CVPR 2019
Susan L. Spraragen
International Conference on Design and Emotion 2010