One of the bottlenecks of example-based machine translation (EBMT) is to be able to amass automatically quantities of good examples. In our work in EBMT, we are investigating how far one can go by performing example extraction from parallel corpora using Probabilistic Translation Dictionaries to obtain example segmentation points. In fact, the success of EBMT highly depends on examples quality and quantity, but also in their length. Thus, we give special importance on methods to extract different size examples from the same translation unit. With this article we show that it is possible to extract quantities for examples from parallel corpora just using probabilistic translation dictionaries extracted from the same corpor
