Multi-Task Sequence Tagging for Emotion-Cause Pair Extraction Via Tag Distribution Refinement

The task emotion-cause pair extraction deals with finding all emotions and the corresponding causes from emotion texts. Existing joint methods solve it as multi-task learning, which introduces two auxiliary tasks (i.e., emotion extraction and cause extraction) to make use of task correlations for their mutual benefits. However, these methods focus on capturing such correlations by sharing parameters in an implicit way, not only have a limitation of cannot explicitly model their information interaction, but also suffer from low interpretability. Towards these issues, we propose a multi-task sequence tagging framework, which can extract emotions with the associated causes simultaneously by encoding their distances into a novel tagging scheme. In addition, the output of both auxiliary tasks can be directly used as inductive bias, to refine the tag distribution for benefiting emotion-cause pair extraction, so that the information exchange between them can be more explicit and interpretable. Results show that our model achieves the best performance, outperforming a number of competitive baselines by at least 1.03% (
$p< 0.01$
) in
$F_1$
score. The comprehensive analysis further confirms the superiority and robustness of our model.