  In recent years, large-scale pre-trained language models (PLMs) have made
extraordinary progress in most NLP tasks. But, in the unsupervised POS tagging
task, works utilizing PLMs are few and fail to achieve state-of-the-art (SOTA)
performance. The recent SOTA performance is yielded by a Guassian HMM variant
proposed by He et al. (2018). However, as a generative model, HMM makes very
strong independence assumptions, making it very challenging to incorporate
contexualized word representations from PLMs. In this work, we for the first
time propose a neural conditional random field autoencoder (CRF-AE) model for
unsupervised POS tagging. The discriminative encoder of CRF-AE can
straightforwardly incorporate ELMo word representations. Moreover, inspired by
feature-rich HMM, we reintroduce hand-crafted features into the decoder of
CRF-AE. Finally, experiments clearly show that our model outperforms previous
state-of-the-art models by a large margin on Penn Treebank and multilingual
Universal Dependencies treebank v2.0.

Computation and Language