Quantifying Synthesis and Fusion and their Impact on Machine Translation. (arXiv:2205.03369v1 [cs.CL])

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 …

arxiv fusion impact machine machine translation translation