When the reaction types are known, HGAR achieves the top-1 accuracy with 65.6%, surpassing RetroPrime and GraphRetro (i.e., the two state-of-the-art semi-template methods) by 1.2% and 2.6%, respectively. Furthermore, it also outperforms template-based and template-free methods. For the top-3, HGAR is inferior to the stateof-the-art methods. While template-based methods perform well in the top-5 and 10 accuracy, because they incorporate knowledge of motif patterns and sacrifice the generalization by establishing dictionaries based on existing data, which requires learning in semitemplate methods. This phenomenon could be attributed to the simplistic approach of jointly training various modules, which leads our model to overly prioritize the primary motifs while inadequately addressing training for other motif patterns.