The biaffine parser of Dozat and Manning (2017) was successfully extended to semantic dependency parsing (SDP) (Dozat and Manning, 2018). Its performance on graphs is surprisingly high given that, without the constraint of producing a tree, all arcs for a given sentence are predicted independently from each other (modulo a shared representation of tokens). To circumvent such an independence of decision, while retaining the O(n^2) complexity and highly parallelizable architecture, we propose to use simple auxiliary tasks that introduce some form of interdependence between arcs. Experiments on the three English acyclic datasets of SemEval 2015 task 18 (Oepen et al., 2015), and on French deep syntactic cyclic graphs (Ribeyre et al., 2014) show modest but systematic performance gains on a near state-of-the-art baseline using transformer-based contextualized representations. This provides a simple and robust method to boost SDP performance.

翻译：Dozat和Manning（2017）提出的双仿射解析器成功扩展至语义依存分析（SDP）（Dozat和Manning，2018）。该模型在图结构上的表现出人意料地高，原因在于其无需生成树结构的约束，给定句子中的所有弧线（基于共享的词语表示）均为独立预测。为解决这种决策独立性，同时保持O(n²)计算复杂度与高度并行化的架构，我们提出采用简单的辅助任务来引入弧线间的某种相互依赖关系。实验基于SemEval 2015任务18的三个英语无环数据集（Oepen等人，2015）以及法语深层句法循环图数据集（Ribeyre等人，2014），结果显示，在使用基于Transformer的上下文表示且接近当前最优水平的基线上，该方法获得了微小但系统性的性能提升。这为提升SDP性能提供了一种简单而稳健的方案。