  Chinese character decomposition has been used as a feature to enhance Machine
Translation (MT) models, combining radicals into character and word level
models. Recent work has investigated ideograph or stroke level embedding.
However, questions remain about different decomposition levels of Chinese
character representations, radical and strokes, best suited for MT. To
investigate the impact of Chinese decomposition embedding in detail, i.e.,
radical, stroke, and intermediate levels, and how well these decompositions
represent the meaning of the original character sequences, we carry out
analysis with both automated and human evaluation of MT. Furthermore, we
investigate if the combination of decomposed Multiword Expressions (MWEs) can
enhance the model learning. MWE integration into MT has seen more than a decade
of exploration. However, decomposed MWEs has not previously been explored.
