CLUZH at SIGMORPHON 2022 Shared Tasks on Morpheme Segmentation and Inflection Generation
Silvan Wehrli, Simon Clematide, Peter Makarov · 2022
This paper describes the submissions of the team of the Department of Computational Linguistics, University of Zurich, to the SIGMOR-PHON 2022 Shared Tasks on Morpheme Segmentation and Inflection Generation.Our submissions use a character-level neural transducer that operates over traditional edit actions.While this model has been found particularly well-suited for low-resource settings, using it with large data quantities has been difficult.Existing implementations could not fully profit from GPU acceleration and did not efficiently implement mini-batch training, which could be tricky for a transition-based system.For this year's submission, we have ported the neural transducer to PyTorch and implemented true mini-batch training.This has allowed us to successfully scale the approach to large data quantities and conduct extensive experimentation.We report competitive results for morpheme segmentation (including sharing first place in part 2 of the challenge).We also demonstrate that reducing sentence-level morpheme segmentation to a word-level problem is a simple yet effective strategy.Additionally, we report strong results in inflection generation (the overall best result for large training sets in part 1, the best results in low-resource learning trajectories in part 2).Our code is publicly available.