Our proposed RACL is a unified multi-task learning framework which enables propagating the interactive relations (denoted as the same R 1 ..R 4 as those in Figure 1) for improving the ABSA performance, and it can be stacked to multiple layers to interact subtasks at different semantic levels.
we present the overall architecture of RACL in a single RACL layer contains three modules: AE, OE, and SC, where each module is designed for the corresponding subtask.