Automatically Learning Source-side Reordering Rules for Large Scale Machine Translation
We describe an approach to automatically learn reordering rules to be applied as a preprocessing step in phrase-based machine translation. We learn rules for 8 different language pairs, showing BLEU improvements for all of them, and demonstrate that many important order transformations (SVO to SOV or VSO, head-modifier, verb movement) can be captured by this approach.
Abstract Power systems in Japan still have a continual upward tendency in size and complexity with increasing demand. Energy control centers have been expanded to cope with requirements by the modern society for the more reliable supply of electric power. Special importance has been attached to the swift and exact switching operation in emergency and restorative states. As it is almost beyond human intervention, tile automated switching operation is under study. Switching operations to be executed at the points of action (power station, substation) will be integrated into a most effective pattern of switching operations under supervision of the points of decision making (central and regional dispatching offices) in the near future.