General-Purpose Communicative Function Recognition using a Hierarchical Network with Cascading Outputs and Maximum a Posteriori Path Estimation
Table 1: Results achieved while predicting Task dimension communicative functions in the DialogBank. TL refers to transfer learning.
['[BOLD] Scenario', '[BOLD] Evaluation', '[BOLD] Prediction', '[BOLD] MR  [BOLD] Base', '[BOLD] MR  [BOLD] TL', '[BOLD] hP  [BOLD] Base', '[BOLD] hP  [BOLD] TL', '[BOLD] hR  [BOLD] Base', '[BOLD] hR  [BOLD] TL', '[BOLD] hF  [BOLD] Base', '[BOLD] hF  [BOLD] TL']
First of all, we can see that using transfer learning improves the performance of every approach, in every scenario, and in terms of every metric. The average improvement is of 12.26 percentage points in terms of exact match ratio and 10.95 percentage points in terms of hierarchical F-measure. This shows that, given the reduced amount of data, training the layers that generate the segment representations solely on the dialogs annotated according to the standard leads to overfitting. On the other hand, since the Switchboard Dialog Act Corpus is sufficiently large, a model trained on its dialogs generates representations that capture information regarding generic intention that is not specific to a single set of labels. The specificities of different sets are then captured by the dimensionality reduction and output layers. In the remainder of this discussion, we focus on the results achieved using transfer learning.