ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification
(c) Performance on the ECE.
['Model', 'Precision', 'Recall', 'F1']
The past clause-level models regarded the ECE task as a set of independent clause classification problems. It is a serious class-imbalance classification problem and the model tends to predict the clause as non-emotion-cause more often. This is also the reason why their Recall scores were quite low (the highest was 75.87). This is because they can capture the relations of multiple clauses which help inferring the current clause. For example, if no other clauses in a document have been detected as an emotion cause, the model will increase the probability of the current clause being predicted as an emotion cause. This finally increases the Recall score. It is clear that by removing the emotion annotations (CANN-E), the F1 score of CANN drops dramatically (about 34.69%). In contrast, our method does not need the emotion annotations and achieve 89.57% in F1 score, which significantly outperforms the CANN-E model by 51.6%. \newcite xia2019emotion guessed that the expression clause extraction and cause clause extraction are not mutually independent. On the one hand, providing emotions can help better discover the causes; on the other hand, knowing causes may also help more accurately extract emotions. Our proposed ETC model uses a classifier to complete the classification of expression and cause, forcing the classifier to learn the intrinsic relationship between them. Thanks to BERT’s self-attention mechanism, our proposed ETC model can capture the relationship between multiple clauses. Compared with Indep, Inter-CE and Inter-EC, our proposed ETC model gets great improvements on the ECPE task as well as the two sub-tasks. Our span-based model achieves 11.57%, 24.77% and 24.5% absolute gains on three sub-task compared to the best classification model, indicating the efficacy of our proposed ETC model.