Finite State Machines
Statistical machine translation models may be implemented using finite state machines, for which a large number of powerful toolkits are available, which provide their own generic decoding algorithms.
Finite State Machines is the main subject of 18 publications.
Instead of devising a dedicated decoding algorithm for statistical machine translation, finite state tools may be used, both for word-based (Bangalore and Riccardi, 2000
; Bangalore and Riccardi, 2001
; Tsukada and Nagata, 2004
; Casacuberta and Vidal, 2004)
, alignment template (Kumar and Byrne, 2003)
and phrase-based models. The use of finite state toolkits also allows for the training of word-based and phrase-based models. The implementation by Deng and Byrne (2005)
is available as the MTTK toolkit (Deng and Byrne, 2006)
. Similarly, the IBM models may be implemented using graphical model toolkits (Filali and Bilmes, 2007)
. Pérez et al. (2007)
compare finite state implementation of word and phrase-based models.
Just as word-based and phrase-based models may be implemented with finite state toolkits, a general framework of tree transducers may subsume many of the proposed tree-based models (Graehl and Knight, 2004)
- Argueta and Chiang (2017)
- Iglesias et al. (2009)
- González and Casacuberta (2009)
- Malik et al. (2010)
- Iglesias et al. (2011)
- Alshawi et al. (2002)
- Beck (2011)
- Vogel and Ney (2000)