Title | ||
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Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging |
Abstract | ||
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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 PLM 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. |
Year | DOI | Venue |
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2022 | 10.18653/v1/2022.findings-acl.259 | FINDINGS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2022) |
DocType | Volume | Citations |
Conference | Findings of the Association for Computational Linguistics: ACL 2022 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
---|---|---|---|
Houquan Zhou | 1 | 0 | 0.68 |
Li Yang | 2 | 134 | 21.12 |
Zhenghua Li | 3 | 325 | 28.48 |
Min Zhang | 4 | 1849 | 157.00 |