论文标题
使用整流器网络学习结构化预测的限制
Learning Constraints for Structured Prediction Using Rectifier Networks
论文作者
论文摘要
各种自然语言处理任务是结构化的预测问题，其中输出是由多个相互依存决定构建的。过去的工作表明，域知识被构成在输出空间上的约束，可以帮助提高预测准确性。但是，设计良好的限制通常依赖于领域的专业知识。在本文中，我们研究了学习这种约束的问题。我们将问题定为训练两层整流器网络以识别有效的结构或子结构，并显示了将训练有素的网络转换为推理变量上线性约束系统的结构。我们对几个NLP任务的实验表明，学习的限制可以提高预测准确性，尤其是在训练示例少时。
Various natural language processing tasks are structured prediction problems where outputs are constructed with multiple interdependent decisions. Past work has shown that domain knowledge, framed as constraints over the output space, can help improve predictive accuracy. However, designing good constraints often relies on domain expertise. In this paper, we study the problem of learning such constraints. We frame the problem as that of training a two-layer rectifier network to identify valid structures or substructures, and show a construction for converting a trained network into a system of linear constraints over the inference variables. Our experiments on several NLP tasks show that the learned constraints can improve the prediction accuracy, especially when the number of training examples is small.
