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.

使用简单的辅助任务来增加语义依存分析（SDP）性能，对英语无环数据集和法语深层语法循环图进行实验，显示了在基于 Transformer 的上下文表示的接近最新技术基线上的有限但系统性能改善，从而提供了一种简单而稳健的提高 SDP 性能的方法。