  This paper introduces SGNMT, our experimental platform for machine
translation research. SGNMT provides a generic interface to neural and symbolic
scoring modules (predictors) with left-to-right semantic such as translation
models like NMT, language models, translation lattices, $n$-best lists or other
kinds of scores and constraints. Predictors can be combined with other
predictors to form complex decoding tasks. SGNMT implements a number of search
strategies for traversing the space spanned by the predictors which are
appropriate for different predictor constellations. Adding new predictors or
decoding strategies is particularly easy, making it a very efficient tool for
prototyping new research ideas. SGNMT is actively being used by students in the
MPhil program in Machine Learning, Speech and Language Technology at the
University of Cambridge for course work and theses, as well as for most of the
research work in our group.
