Abstract
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.- Anthology ID:
- 2022.findings-acl.259
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2022
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3276–3290
- Language:
- URL:
- https://aclanthology.org/2022.findings-acl.259
- DOI:
- 10.18653/v1/2022.findings-acl.259
- Cite (ACL):
- Houquan Zhou, Yang Li, Zhenghua Li, and Min Zhang. 2022. Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging. In Findings of the Association for Computational Linguistics: ACL 2022, pages 3276–3290, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging (Zhou et al., Findings 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.findings-acl.259.pdf
- Code
- Jacob-Zhou/FeatureCRFAE
- Data
- Penn Treebank, Universal Dependencies