Improving Implicit Discourse Relation Classification by Modeling Inter-dependencies of Discourse Units in a Paragraph
Table 3: Multi-class Classification Results on PDTB. We report accuracy (Acc) and macro-average F1-scores for both explicit and implicit discourse relation predictions. We also report class-wise F1 scores.
['Model', 'Implicit Macro', 'Implicit Acc', 'Implicit Comp', 'Implicit Cont', 'Implicit Exp', 'Implicit Temp', 'Explicit Macro', 'Explicit Acc']
Multi-way Classification: 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. The first row shows the performance of a variant of our basic model, where we only identify implicit relations and ignore identifying explicit relations by setting the α in equation (5) to be 0. Compared with the baseline Bi-LSTM model, the only difference is that this model considers paragraph-wide contexts and model inter-dependencies among discourse units when building representation for individual DU. We can see that this model has greatly improved implicit relation classification performance across all the four relations and improved the macro-average F1-score by over 7 percents. In addition, compared with the baseline Bi-LSTM model with tensor, this model improved implicit relation classification performance across the three small classes, with clear performance gains of around 2 and 8 percents on contingency and temporal relations respectively, and overall improved the macro-average F1-score by 2.2 percents.