用于半监督学习的变分序列标记算法
Variational Sequential Labelers for Semi-Supervised Learning
摘要：
我们介绍了一个家庭的半监督序列标记的多任务变分方法。我们的模型家族包括一个潜变量生成模型和一个判别标签。生成模型使用潜变量来定义给定上下文的单词的条件概率，从学习词嵌入中常用的单词预测目标中汲取灵感。标签器有助于将区分信息注入潜在空间。我们探讨了几个潜在的变量配置，包括具有层次结构，这使得该模型能够考虑特定于标签和特定于单词的信息。我们的模型在8个序列标记数据集上的表现始终优于标准序列基线，并在未标记数据上进一步改进。
英文摘要：
We introduce a family of multitask variational methods for semi-supervised sequence labeling. Our model family consists of a latent-variable generative model and a discriminative labeler. The generative models use latent variables to define the conditional probability of a word given its context, drawing inspiration from word prediction objectives commonly used in learning word embeddings. The labeler helps inject discriminative information into the latent space. We explore several latent variable configurations, including ones with hierarchical structure, which enables the model to account for both label-specific and word-specific information. Our models consistently outperform standard sequential baselines on 8 sequence labeling datasets, and improve further with unlabeled data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
