Morphological Inflection: A Reality Check
Jordan Kodner Sarah Payne Salam Khalifa Zoey Liu
Morphological inflection is a popular task in sub-word NLP with both
practical and cognitive applications. For years now, state-of-the-art systems
have reported high, but also highly variable, performance across data sets and
languages. We investigate the causes of this high performance and high
variability; we find several aspects of data set creation and evaluation which
systematically inflate performance and obfuscate differences between languages.
To improve generalizability and reliability of results, we propose new data
sampling and evaluation strategies that better reflect likely use-cases. Using
these new strategies, we make new observations on the generalization abilities
of current inflection systems.