RAP-Net: Recurrent Attention Pooling Networks for Dialogue Response Selection
Table 3: Official rankings of our systems on each subtask. The overall ranking considers all subtasks.
['[EMPTY]', 'Ubuntu', 'Advising']
In the DSTC7 challenge, the proposed systems are submitted for official evalution. For each subtask, our submitted system consists of several models with different hyperparameters and auxiliary features. Using different features gives our model multiple perspectives to the data and hence improves the prediction accuracy. Note that the model for the subtask 5 is the single model, which performs worse than the ensemble one as the subtask 1. In the official evaluation, the superior performance and the achieved rankings across different subtasks significantly demonstrate the effectiveness of the proposed model. Considering that our rankings are either 2 or 3 among 20 teams, we argue that the proposed RAP-Net can successfully estimate the relatedness between dialogues and responses and generalize across different datasets.