AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue
Table 2: Precision @1 comparison between different methods. Precision@1 % tell us the number of times the correct response from the dataset comes up. Details in Section 5.4.2
['[BOLD] Method', '[BOLD] Precision@1']
Accuracy on this binary classification problem has been used to select the best network. Furthermore, we perform ablation studies using different modules to understand the effect of each component in the network. A 4 layer neural network with ReLU activation in its hidden layers and softmax in the final layer is used as the classifier. External knowledge in conjunction with memory and GCN module has the best accuracy when embeddings of query and response are concatenated together. This is another metric used to judge the effectiveness of our network. It is different from the next sentence prediction task accuracy. It measures that for n trials, the number of times a relevant response is reported with the highest confidence value.