Transformers on Multilingual Clause-Level Morphology
Emre Can Acikgoz, Tilek Chubakov, Müge Kural, Gözde Gül Şahin, Deniz Yüret · 2022
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. 1