Publication at Faculty of Mathematics and Physics |
2014
Abstract
Current state-of-the-art statistical machine translation (SMT) relies on simple feature functions which make independence assumptions at the level of phrases or CFG rules. However, it is well-known that discriminative models can benefit from rich features extracted from the source sentence context outside of the applied phrase or CFG rule, which is available at decoding time.
We present a framework for the open-source decoder Moses that allows discriminative models over source context to easily be trained on a large number of examples and then be included as feature functions in decoding.