论文标题
部分可观测时空混沌系统的无模型预测
Improving Low-Resource Cross-lingual Parsing with Expected Statistic Regularization
论文作者
论文摘要
我们提出了预期的统计正则化（ESR），这是一种新型的正则化技术，利用低阶多任务结构统计数据来塑造低资源数据集中半监督学习的模型分布。我们在跨语性转移的背景下研究ESR，以进行句法分析（POS标记和标记的依赖性解析），并介绍了对模型行为的几类低阶统计函数。在实验上，我们对5种不同目标语言的无监督转移评估了所提出的统计数据，并表明，当准确估计所有统计数据时，对POS和LAS的估计都会提高，最佳统计量将POS提高+7.0，而LAS则平均将LAS提高到+8.5。我们还提出了半监督的转移和学习曲线实验，该实验表明ESR可为适度的标签数据提供强大的跨语言转移 - 加密 - 加密调整基线。这些结果表明，ESR是用于交叉解析的模型转移方法的一种有前途和互补的方法。
We present Expected Statistic Regularization (ESR), a novel regularization technique that utilizes low-order multi-task structural statistics to shape model distributions for semi-supervised learning on low-resource datasets. We study ESR in the context of cross-lingual transfer for syntactic analysis (POS tagging and labeled dependency parsing) and present several classes of low-order statistic functions that bear on model behavior. Experimentally, we evaluate the proposed statistics with ESR for unsupervised transfer on 5 diverse target languages and show that all statistics, when estimated accurately, yield improvements to both POS and LAS, with the best statistic improving POS by +7.0 and LAS by +8.5 on average. We also present semi-supervised transfer and learning curve experiments that show ESR provides significant gains over strong cross-lingual-transfer-plus-fine-tuning baselines for modest amounts of label data. These results indicate that ESR is a promising and complementary approach to model-transfer approaches for cross-lingual parsing.
