论文标题
几何学意识与异质动态卷积
Geometry-Aware Supertagging with Heterogeneous Dynamic Convolutions
论文作者
论文摘要
类别语法形式主义的句法类别是由较小的，不可分割的原语制成的结构性单位，并由基本语法类别的形成规则束缚在一起。在建设性超级绘制的趋势方法中，神经模型越来越多地意识到内部类别结构，这反过来又使他们能够更可靠地预测稀有和销量范围内的类别类别，对先前认为太复杂而无法找到实际使用的语法具有重大影响。在这项工作中，我们从图理论的角度重新审视了建设性的超级插曲，并提出了一个基于旨在利用Supertagger输出空间独特结构的异质动态图卷积的框架。我们在许多涵盖不同语言和语法形式主义的类别语法数据集上测试了我们的方法，对先前的艺术成绩进行了实质性改进。代码将在https://github.com/konstantinoskokos/dynamic-graph-supertagging上提供
The syntactic categories of categorial grammar formalisms are structured units made of smaller, indivisible primitives, bound together by the underlying grammar's category formation rules. In the trending approach of constructive supertagging, neural models are increasingly made aware of the internal category structure, which in turn enables them to more reliably predict rare and out-of-vocabulary categories, with significant implications for grammars previously deemed too complex to find practical use. In this work, we revisit constructive supertagging from a graph-theoretic perspective, and propose a framework based on heterogeneous dynamic graph convolutions aimed at exploiting the distinctive structure of a supertagger's output space. We test our approach on a number of categorial grammar datasets spanning different languages and grammar formalisms, achieving substantial improvements over previous state of the art scores. Code will be made available at https://github.com/konstantinosKokos/dynamic-graph-supertagging
