We evaluate three leading dependency parser systems from different paradigms on a small yet diverse subset of languages in terms of their accuracy-efficiency Pareto front. As we are interested in efficiency, we evaluate core parsers without pretrained language models (as these are typically huge networks and would constitute most of the compute time) or other augmentations that can be transversally applied to any of them. Biaffine parsing emerges as a well-balanced default choice, with sequence-labelling parsing being preferable if inference speed (but not training energy cost) is the priority.

翻译：我们从不同模式中评估了三种主要依赖分析系统,从精度效率的角度来评估少数但多样化的亚类语言。 由于我们对效率感兴趣,我们评估核心分析者时没有经过预先培训的语言模式(因为这些模式通常是庞大的网络,将构成计算时间的大部分 ), 或其他可横向适用于其中任何一个语言的增强系统。 双硫酸分解作为一种平衡的默认选择出现,如果推论速度(但不包括能源成本培训)为优先事项,则以序列标签划分为优先,更可取。