  This paper describes our winning systems in MRL: The 1st Shared Task on
Multilingual Clause-level Morphology (EMNLP 2022 Workshop) designed by KUIS AI
NLP team. We present our work for all three parts of the shared task:
inflection, reinflection, and analysis. We mainly explore transformers with two
approaches: (i) training models from scratch in combination with data
augmentation, and (ii) transfer learning with prefix-tuning at multilingual
morphological tasks. Data augmentation significantly improves performance for
most languages in the inflection and reinflection tasks. On the other hand,
Prefix-tuning on a pre-trained mGPT model helps us to adapt analysis tasks in
low-data and multilingual settings. While transformer architectures with data
augmentation achieved the most promising results for inflection and
reinflection tasks, prefix-tuning on mGPT received the highest results for the
analysis task. Our systems received 1st place in all three tasks in MRL 2022.
