Chinese character decomposition has been used as a feature to enhance Machine Translation (MT) models, combining rad- icals into character and word level mod- els. Recent work has investigated ideo- graph or stroke level embedding. How- ever, questions remain about different de- composition 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 intermedi- ate levels, and how well these decomposi- tions represent the meaning of the original character sequences, we carry out analy- sis with both automated and human evalu- ation of MT. Furthermore, we investigate if the combination of decomposed Mul- tiword Expressions (MWEs) can enhance the model learning. MWE integration into MT has seen more than a decade of explo- ration. However, decomposed MWEs has not previously been explored.