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.

通过量化语言单词和片段级别的形态学来降低整体语言严格形态类型的刻板印象，测试了无监督和监督的形态分割方法，并提出了一种半自动的融合方法，探讨了机器翻译质量与合成和融合程度之间的关系，并在英语、德语、土耳其和西班牙语上进行了实验。