Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes
Table 1: Dialog-acts prediction accuracy in Reddit validation set.
['Model', 'Acc(%)']
In this section, we present the experiments with the model architectures for the dialog attribute prediction - dialog-acts from Reddit, Switchboard and Frames datasets. The model F(Ut) refers to the architecture which predicts the dialog-acts based on current utterance Ut alone. The tokens in the current utterance Ut are fed through a two-layer GRU and the final hidden state is used to predict the dialog-acts. The model F(DAt−1,t−2) predicts the current utterance’s dialog-acts DAt based on the dialog-acts corresponding to the previous two utterances. We consider the dialog-acts prediction problem as a sequence modelling problem where we feed the dialog-acts into a single-layer GRU and predict the current dialog-acts conditioned on the previous dialog-acts. We settled on conditioning on the dialog-acts corresponding to the previous two utterances alone as we didn’t observe any boost in the classifier performance from the older dialog-acts.