.. SGNMT documentation master file, created by sphinx-quickstart on Tue May 17 17:32:32 2016. You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. 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: - `Tensor2Tensor `_ (`TensorFlow `_) - `fairseq `_ (`PyTorch `_) Old SGNMT versions (0.x) are compatible with: - `(extended) TF seq2seq tutorial `_ (`TensorFlow `_) - `Blocks `_ (`Theano `_) Contents ------------- .. toctree:: :maxdepth: 1 setup tutorial tutorial_pytorch adding_components bea19_gec tutorial_blocks command_line predictors decoders examples faq publications All modules 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. Indices and tables ================== * :ref:`genindex` * :ref:`modindex`