Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes
Table 3: Perplexity and Embedding Metrics for the Reddit validation set.
['Metric', 'LM', 'Seq2Seq', 'Seq2Seq+Attr']
Reddit: First, we evaluate Seq2Seq models trained on the manually annotated Reddit corpus as shown in Seq2Seq+Attr refers to our model where we condition on the dialog-acts additionally. Please note that we use the notation ”Attr” here to maintain generality as it may refer to other dialog attributes like sentiment later in this section. For both the baseline and conditional Seq2Seq models, we consider a dialog context involving the previous two turns as we did not observe significant performance improvement with three or more turns. We use a 2-layer GRU language model as a baseline for comparison. Higher perplexity observed in the Reddit corpus could be due to the presence of several topics in the dataset (exhibits high entropy) and fewer dialogs compared to other open domain dialog datasets.