Emotion-cause pair extraction method based on multi-granularity
  information and multi-module interaction
Mingrui Fu Weijiang Li
The purpose of emotion-cause pair extraction is to extract the pair of
emotion clauses and cause clauses. On the one hand, the existing methods do not
take fully into account the relationship between the emotion extraction of two
auxiliary tasks. On the other hand, the existing two-stage model has the
problem of error propagation. In addition, existing models do not adequately
address the emotion and cause-induced locational imbalance of samples. To solve
these problems, an end-to-end multitasking model (MM-ECPE) based on shared
interaction between GRU, knowledge graph and transformer modules is proposed.
Furthermore, based on MM-ECPE, in order to use the encoder layer to better
solve the problem of imbalanced distribution of clause distances between
clauses and emotion clauses, we propose a novel encoding based on BERT,
sentiment lexicon, and position-aware interaction module layer of emotion motif
pair retrieval model (MM-ECPE(BERT)). The model first fully models the
interaction between different tasks through the multi-level sharing module, and
mines the shared information between emotion-cause pair extraction and the
emotion extraction and cause extraction. Second, to solve the imbalanced
distribution of emotion clauses and cause clauses problem, suitable labels are
screened out according to the knowledge graph path length and task-specific
features are constructed so that the model can focus on extracting pairs with
corresponding emotion-cause relationships. Experimental results on the ECPE
benchmark dataset show that the proposed model achieves good performance,
especially on position-imbalanced samples.