The Machine Translation Toolpack for LoonyBin: Automated Management of Experimental Machine Translation HyperWorkflows

Construction of machine translation systems has evolved into a multi-stage workflow involving many complicated dependencies. Many decoder distributions have addressed this by including monolithic training scripts - train-factored-model.pl for Moses and mr_runmer.pl for SAMT. However, such scripts can be tricky to modify for novel experiments and typically have limited support for the variety of job schedulers found on academic and commercial computer clusters. Further complicating these systems are hyperparameters, which often cannot be directly optimized by conventional methods requiring users to determine which combination of values is best via trial and error. The recently-released LoonyBin open-source workflow management tool addresses these issues by providing: 1) a visual interface for the user to create and modify workflows; 2) a well-defined logging mechanism; 3) a script generator that compiles visual workflows into shell scripts, and 4) the concept of Hyperworkflows, which intuitively and succinctly encodes small experimental variations within a larger workflow. In this paper, we describe the Machine Translation Toolpack for LoonyBin, which exposes state-of-the-art machine translation tools as drag-and-drop components within LoonyBin.