A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks
Table 2: Accuracy of the dialogue act identification using the character-level language model utterance representation for 42 classes using a single MLP layer with 64 neurons.
['Model input', 'Acc.(%)', 'SD']
The hidden vector st obtained after the last character is called the last feature vector, as it stores the information related to the character language model and the sentiment of the utterance. However, it was shown that the average vector over all characters in the utterance works better for emotion detection Hence, we extract the last feature vector and also the average feature vector representations for each utterance. The standard deviation (SD) is computed over ten runs. The average vector seems to carry more information related to the DA; hence we use it for future experiments. There is an advantage of using domain-independent data: it is rich regarding features being trained on big data, perhaps surpassing the limitation of scalability as mentioned in Ji et al. (2016).