A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
Table 3: Accuracy of the dialogue act identification with the context-learning approach.
['Model setup', 'Acc.(%)', 'SD']
We examined different values for the number of the hidden units of the RNN, empirically 64 was identified as best and used throughout the experiments. We also experimented with the various representations for the speaker id that is concatenated with the respective utterances but could find no differences. As a result, our proposed model uses minimal information for the context. The performance increases from 74% to about 77% with context. We run each experiment for ten times and take the average. The model shows robustness providing minimal variance, and using a minimum number of preceding utterances as a context can produce fair results.