SIGMORPHON 2022 Task 0 Submission Description: Modelling Morphological Inflection with Data-Driven and Rule-Based Approaches

Abstract

This paper describes our participation in the 2022 SIGMORPHON-UniMorph Shared Task on Typologically Diverse and Acquisition-Inspired Morphological Inflection Generation. We present two approaches: one being a modification of the neural baseline encoder-decoder model, the other being hand-coded morphological analyzers using finite-state tools (FST) and outside linguistic knowledge. While our proposed modification of the baseline encoder-decoder model underperforms the baseline for almost all languages, the FST methods outperform other systems in the respective languages by a large margin. This confirms that purely data-driven approaches have not yet reached the maturity to replace trained linguists for documentation and analysis especially considering low-resource and endangered languages.