Unsupervised learning of morphology

Unsupervised learning of morphological rules is one of the expected abilities of natural language processing (NLP) models since children learn these rules during their native language acquisition without supervision. Based on this expectation, we present a comprehensive experimental setup for evaluating the morphological learning of several unsupervised models such as Autoencoders (AE), Variational Autoencoders (VAE), Character-level Language Models (CharLM) and Vector Quantized Variational Autoencoders (VQVAE) at the following tasks: probing for morphological features, morphological segmentation and morphological reinflection. In our study, we show that for probing, all models outperform baselines with an indication of encoding morphological knowledge; for morphological segmentation, VAE and CharLMs have comparable performances to unsupervised SOTA models; for morphological reinflection, VQVAE with multiple codebooks has the ability to identify the lemma and suffixes of a word and turns out to be a good candidate to perform inflectional tasks.