  Emotion cause pair extraction (ECPE), as one of the derived subtasks of
emotion cause analysis (ECA), shares rich inter-related features with emotion
extraction (EE) and cause extraction (CE). Therefore EE and CE are frequently
utilized as auxiliary tasks for better feature learning, modeled via multi-task
learning (MTL) framework by prior works to achieve state-of-the-art (SoTA) ECPE
results. However, existing MTL-based methods either fail to simultaneously
model the specific features and the interactive feature in between, or suffer
from the inconsistency of label prediction. In this work, we consider
addressing the above challenges for improving ECPE by performing two alignment
mechanisms with a novel A^2Net model. We first propose a feature-task alignment
to explicitly model the specific emotion-&cause-specific features and the
shared interactive feature. Besides, an inter-task alignment is implemented, in
which the label distance between the ECPE and the combinations of EE&CE are
learned to be narrowed for better label consistency. Evaluations of benchmarks
show that our methods outperform current best-performing systems on all ECA
subtasks. Further analysis proves the importance of our proposed alignment
mechanisms for the task.
