SGNMT is an open-source framework for neural machine translation (NMT). 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 lattice rescoring. A wide variety of search strategies is available for complex decoding problems. SGNMT is compatible with Blocks/Theano and TensorFlow. Features:

• Syntactically guided neural machine translation (NMT lattice rescoring)
• NMT support for Blocks/Theano and TensorFlow
• n-best list rescoring with NMT
• 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
• NMT training with options for reshuffling and fixing word embeddings
• Bag2Sequence models and decoding algorithms
• Custom distributed word representations
• Joint decoding with word- and subword/character-level models
• Hypothesis recombination
• Heuristic search
• Neural word alignment
• ...

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, Aurelien Waite, and Bill Byrne. Syntactically guided neural machine translation. In Proceedings of the 54th annual meeting of the Association for Computational Linguistics (ACL 16), August 2016. Berlin, Germany. arXiv