This paper presents an interpretable unsupervised morphological learning model, showing comparable performance to supervised models in learning complex morphological rules of Turkish as evidenced by its application to the problem of morphological inflection within the SIGMORPHON Shared Tasks. The significance of our unsupervised approach lies in its alignment with how humans naturally acquire rules from raw data without supervision. To achieve this, we construct a model with multiple codebooks of VQ-VAE employing continuous and discrete latent variables during word generation. We evaluate the model’s performance under high and low-resource scenarios, and use probing techniques to examine encoded information in latent representations. We also evaluate its generalization capabilities by testing unseen suffixation scenarios within the SIGMORPHON-UniMorph 2022 Shared Task 0. Our results demonstrate our model’s ability to distinguish word structures into lemmas and suffixes, with each codebook specialized for different morphological features, contributing to the interpretability of our model and effectively performing morphological inflection on both seen and unseen morphological features.

This paper describes the KUIS-AI NLP team’s submission for the 1st Shared Task on Multilingual Clause-level Morphology (MRL2022). We present our work on all three parts of the shared task: inflection, reinflection, and analysis. We mainly explore two approaches: Trans- former models in combination with data augmentation, and exploiting the state-of-the-art language modeling techniques for morphological analysis. Data augmentation leads to a remarkable performance improvement for most of the languages in the inflection task. Prefix-tuning on pretrained mGPT model helps us to adapt reinflection and analysis tasks in a low-data setting. Additionally, we used pipeline architectures using publicly available open-source lemmatization tools and monolingual BERT- based morphological feature classifiers for rein- flection and analysis tasks, respectively. While Transformer architectures with data augmentation and pipeline architectures achieved the best results for inflection and reinflection tasks, pipelines and prefix-tuning on mGPT received the highest results for the analysis task. Our methods achieved first place in each of the three tasks and outperforms mT5-baseline with 89% for inflection, 80% for reflection, and 12% for analysis. Our code 1 is publicly available.