Example 11 shows the advantage of combining the vector representation with orthographic distance, i.e., our model could find translations of sleddogs that have similar meaning, while in examples 12 and 13 orthographic distance helped to pick the correct translation which is the closest in terms of edit distance.
in example 14 orthographic distance caused an error because the incorrect prediction is too close to the source word in orthographic distance.