SGNMT¶
SGNMT is an open-source framework for neural machine translation (NMT) and other sequence prediction tasks. The tool provides a flexible platform which allows pairing NMT with various other models such as language models, length models, or bag2seq models. It supports rescoring both n-best lists and lattices. A wide variety of search strategies is available for complex decoding problems.
SGNMT is compatible with the following NMT toolkits:
Old SGNMT versions (0.x) are compatible with:
Contents¶
Features¶
- Syntactically guided neural machine translation (NMT lattice rescoring)
- Target-side syntax for NMT
- n-best list rescoring with NMT
- Integrating external n-gram posterior probabilities used in MBR
- Ensemble NMT decoding
- Forced NMT decoding
- Integrating language models
- Different search algorithms (beam, A*, depth first search, greedy...)
- Target sentence length modelling
- Bag2Sequence models and decoding algorithms
- Joint decoding with word- and subword/character-level models
- Hypothesis recombination
- Heuristic search
- ...
Project links¶
- Issue tracker: http://github.com/ucam-smt/sgnmt/issues
- Source code: http://github.com/ucam-smt/sgnmt
Contributors¶
- Felix Stahlberg, University of Cambridge
- Eva Hasler, SDL Research
- Danielle Saunders, University of Cambridge
License¶
The project is licensed under the Apache 2 license.