Abstract
This paper presents a novel online relevant set algorithm for a linearly scored block sequence translation model. The key component is a new procedure to directly optimize the global scoring function used by a statistical machine translation (SMT) decoder. This training procedure treats the decoder as a black-box, and thus can be used to optimize any decoding scheme. The novel algorithm is evaluated using different feature types: 1) commonly used probabilistic features, such as translation, language, or distortion model probabilities, and 2) binary features. In particular, encouraging results on a standard Arabic-English translation task are presented for a translation system that uses only binary feature functions. To further demonstrate the effectiveness of the novel training algorithm, a detailed comparison with the widely used minimum-error-rate (MER) training algorithm is presented using the same decoder and feature set. The online algorithm is simplified by introducing so-called "seed" block sequences which enable the training to be carried out without a gold standard block translation. While the online training algorithm is extremely fast, it also improves translation scores over the MER algorithm in some experiments.
Original language | English (US) |
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Pages (from-to) | 1274-1286 |
Number of pages | 13 |
Journal | IEEE Transactions on Audio, Speech and Language Processing |
Volume | 16 |
Issue number | 7 |
DOIs | |
State | Published - Sep 2008 |
Externally published | Yes |
Keywords
- Discriminative learning
- Online algorithm
- Statistical machine translation
ASJC Scopus subject areas
- Acoustics and Ultrasonics
- Electrical and Electronic Engineering