  Theoretical work in morphological typology offers the possibility of
measuring morphological diversity on a continuous scale. However, literature in
Natural Language Processing (NLP) typically labels a whole language with a
strict type of morphology, e.g. fusional or agglutinative. In this work, we
propose to reduce the rigidity of such claims, by quantifying morphological
typology at the word and segment level. We consider Payne (2017)'s approach to
classify morphology using two indices: synthesis (e.g. analytic to
polysynthetic) and fusion (agglutinative to fusional). For computing synthesis,
we test unsupervised and supervised morphological segmentation methods for
English, German and Turkish, whereas for fusion, we propose a semi-automatic
method using Spanish as a case study. Then, we analyse the relationship between
machine translation quality and the degree of synthesis and fusion at word
(nouns and verbs for English-Turkish, and verbs in English-Spanish) and segment
level (previous language pairs plus English-German in both directions). We
complement the word-level analysis with human evaluation, and overall, we
observe a consistent impact of both indexes on machine translation quality.
