Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph
Table 5: Multi-class Classification Results of Ensemble Models on PDTB.
['Model', 'Implicit Macro', 'Implicit Acc', 'Explicit Macro', 'Explicit Acc']
As we explained in section 4.2, we ran our models for 10 times to obtain stable average performance. Then we also created ensemble models by applying majority voting to combine results of ten runs. The full model achieves performance boosting of (51.84 - 48.82 = 3.02) and (94.17 - 93.21 = 0.96) in macro F1-scores for predicting implicit and explicit discourse relations respectively. Furthermore, the ensemble model achieves the best performance for predicting both implicit and explicit discourse relations simultaneously.