Variational Neural Discourse Relation Recognizer
(c) Exp vs Other
['[BOLD] Model', '[BOLD] Acc', '[BOLD] P', '[BOLD] R', '[BOLD] F1']
Because the development and test sets are imbalanced in terms of the ratio of positive and negative instances, we chose the widely-used F1 score as our major evaluation metric. In addition, we also provide the precision, recall and accuracy for further analysis. according to their F1 scores. Although it fails on Con, VarNDRR achieves the best result on EXP/Com among these three models. Overall, VarNDRR is competitive in comparison with these two baselines. With respect to the accuracy, our model does not yield substantial improvements over the two baselines. This may be because that we used the F1 score rather than the accuracy, as our selection criterion on the development set. With respect to the precision and recall, our model tends to produce relatively lower precisions but higher recalls. This suggests that the improvements of VarNDRR in terms of F1 scores mostly benefits from the recall values.