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 multiple NMT implementations based on Theano (Blocks) and TensorFlow (the extended seq2seq tutorial and tensor2tensor).
- Syntactically guided neural machine translation (NMT lattice rescoring)
- NMT support in Theano (Blocks) and TensorFlow (Tensor2Tensor)
- n-best list rescoring with NMT
- Integrating external n-gram posterior probabilities used in MBR
- Ensemble NMT decoding
- Forced NMT decoding
- Integrating language models (Kneser-Ney, NPLM, RNNLM)
- 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
- Extensions to NMT training in Blocks (reshuffling, fixing and customizing word embeddings, ...)
- Neural word alignment (Blocks/Theano only)
- ...
Please see the full SGNMT documentation for more information.
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