Question: What discourse relations does it work best/worst for?

Evidence:
raw_evidence: The second row shows the performance of our basic paragraph-level model which predicts both implicit and explicit discourse relations in a paragraph. Compared to the variant system (the first row), the basic model further improved the classification performance on the first three implicit relations. Especially on the contingency relation, the classification performance was improved by another 1.42 percents. Moreover, the basic model yields good performance for recognizing explicit discourse relations as well, which is comparable with previous best result (92.05% macro F1-score and 93.09% accuracy as reported in BIBREF11 ).
After untying parameters in the softmax prediction layer, implicit discourse relation classification performance was improved across all four relations, meanwhile, the explicit discourse relation classification performance was also improved. The CRF layer further improved implicit discourse relation recognition performance on the three small classes. In summary, our full paragraph-level neural network model achieves the best macro-average F1-score of 48.82% in predicting implicit discourse relations, which outperforms previous neural tensor network models (e.g., BIBREF18 ) by more than 2 percents and outperforms the best previous system BIBREF19 by 1 percent.
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. From table 5 , each ensemble model obtains performance improvements compared with single model. 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.
raw_evidence: FLOAT SELECTED: Table 3: Multi-class Classification Results on PDTB. We report accuracy (Acc) and macro-average F1scores for both explicit and implicit discourse relation predictions. We also report class-wise F1 scores.
The Penn Discourse Treebank (PDTB): We experimented with PDTB v2.0 BIBREF7 which is the largest annotated corpus containing 36k discourse relations in 2,159 Wall Street Journal (WSJ) articles. In this work, we focus on the top-level discourse relation senses which are consist of four major semantic classes: Comparison (Comp), Contingency (Cont), Expansion (Exp) and Temporal (Temp). We followed the same PDTB section partition BIBREF12 as previous work and used sections 2-20 as training set, sections 21-22 as test set, and sections 0-1 as development set. Table 1 presents the data distributions we collected from PDTB.
Multi-way Classification: The first section of table 3 shows macro average F1-scores and accuracies of previous works. The second section of table 3 shows the multi-class classification results of our implemented baseline systems. Consistent with results of previous works, neural tensors, when applied to Bi-LSTMs, improved implicit discourse relation prediction performance. However, the performance on the three small classes (Comp, Cont and Temp) remains low.

Answers:
explicit discourse relations
Best: Expansion (Exp). Worst: Comparison (Comp).