Gang Liu, Michael Sun, et al.
ICLR 2025
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).
Gang Liu, Michael Sun, et al.
ICLR 2025
Imran Nasim, Melanie Weber
SCML 2024
Rangachari Anand, Kishan Mehrotra, et al.
IEEE Transactions on Neural Networks
Rei Odaira, Jose G. Castanos, et al.
IISWC 2013