Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging
This work addresses the challenge of improving unsupervised POS tagging for NLP applications, representing an incremental advancement by combining existing techniques in a novel way.
The authors tackled the problem of unsupervised part-of-speech tagging by proposing a neural conditional random field autoencoder (CRF-AE) model that integrates pre-trained language models and hand-crafted features, achieving state-of-the-art performance with significant improvements on Penn Treebank and multilingual Universal Dependencies treebank v2.0.
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.