论文标题
桥接预训练的语言模型和手工制作的功能，用于无监督的POS标签
Bridging Pre-trained Language Models and Hand-crafted Features for Unsupervised POS Tagging
论文作者
论文摘要
近年来，大规模训练的语言模型（PLM）在大多数NLP任务中取得了非凡的进步。但是，在无监督的POS标记任务中，使用PLM的工作很少，并且无法实现最新的（SOTA）性能。 He等人提出的一种Guassian HMM变体产生了最近的SOTA性能。 （2018）。但是，作为一种生成模型，HMM做出了非常强大的独立性假设，因此将PLM的contexualized单词表示非常具有挑战性。在这项工作中，我们首次提出了无监督POS标记的神经条件随机场自动编码器（CRF-AE）模型。 CRF-AE的歧视性编码器可以直接合并Elmo单词表示。此外，受到功能丰富的HMM的启发，我们将手工制作的功能重新引入了CRF-AE的解码器。最后，实验清楚地表明，我们的模型在宾夕法尼亚州立树库和多语言通用依赖性Treebank v2.0上的优势优于先前的最先进模型。
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.
