论文标题
与平均场推理的二阶语义依赖性解析的建模标签相关性
Modeling Label Correlations for Second-Order Semantic Dependency Parsing with Mean-Field Inference
论文作者
论文摘要
二阶语义解析具有端到端平均场推理的表现良好。在这项工作中，我们旨在通过建模相邻弧之间的标签相关性来改善此方法。但是，直接建模会导致内存爆炸，因为二阶得分张量的尺寸为$ O（n^3l^2）$（$ n $是句子长度，$ l $是标签的数量），这是不起作用的。为了应对这一计算挑战，我们利用张量分解技术，有趣的是，我们表明，在均值场推理期间，大型二阶得分张量无需实现，从而降低了从立方体到二次的计算复杂性。我们在Semeval 2015 Task 18英语数据集上进行实验，显示了建模标签相关性的有效性。我们的代码可在https://github.com/sustcsonglin/mean-field-dep-parsing上公开获取。
Second-order semantic parsing with end-to-end mean-field inference has been shown good performance. In this work we aim to improve this method by modeling label correlations between adjacent arcs. However, direct modeling leads to memory explosion because second-order score tensors have sizes of $O(n^3L^2)$ ($n$ is the sentence length and $L$ is the number of labels), which is not affordable. To tackle this computational challenge, we leverage tensor decomposition techniques, and interestingly, we show that the large second-order score tensors have no need to be materialized during mean-field inference, thereby reducing the computational complexity from cubic to quadratic. We conduct experiments on SemEval 2015 Task 18 English datasets, showing the effectiveness of modeling label correlations. Our code is publicly available at https://github.com/sustcsonglin/mean-field-dep-parsing.
