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:
Features:
- Syntactically guided neural machine translation (NMT lattice rescoring)
- 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
- ...
Please see the full SGNMT documentation for more information.
- Felix Stahlberg, University of Cambridge
- Eva Hasler, SDL Research
- Danielle Saunders, University of Cambridge
If you use SGNMT in your work, please cite the following paper:
Felix Stahlberg, Eva Hasler, Danielle Saunders, and Bill Byrne. SGNMT - A Flexible NMT Decoding Platform for Quick Prototyping of New Models and Search Strategies. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing (EMNLP 17 Demo Session), September 2017. Copenhagen, Denmark. arXiv