用序列模型联合建模多标签相关性，自动满足逻辑约束。
Constraint-aware Learning of Probabilistic Sequential Models for Multi-Label Classification
- 将单标签分类器输出输入序列模型，生成联合概率分布
- 训练时利用标签间逻辑关系提升性能，推理时强制满足约束
- 适合标签间存在依赖关系的复杂多标签场景
针对包含大量标签的多标签分类任务，若输出标签间存在已知逻辑约束，本文提出一种架构：将各标签分类器输出输入到一个表达能力强的序列模型中，生成联合分布。该模型能有效捕捉由约束引发的标签相关性。实验表明，该方法在训练阶段可利用约束信息提升效果，在推理阶段能严格遵守约束条件。
原文摘要 · Abstract (English)
We investigate multi-label classification involving large sets of labels, where the output labels may be known to satisfy some logical constraints. We look at an architecture in which classifiers for individual labels are fed into an expressive sequential model, which produces a joint distribution. One of the potential advantages for such an expressive model is its ability to modelling correlations, as can arise from constraints. We empirically demonstrate the ability of the architecture both to exploit constraints in training and to enforce constraints at inference time.
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