Teaching Machines to Converse
Table 4.3: Perplexity for standard Seq2Seq and the Speaker model on the development set of the Twitter Persona dataset.
['Model', 'Standard LSTM', 'Speaker Model']
We observe about a 10% decrease in perplexity for the Speaker model over the standard Seq2Seq model. It is worth noting that our persona models are more beneficial to the MLE models than to the MMI models. This result is intuitive as the persona models help make Standard LSTM MLE outputs more informative and less bland, and thus make the use of MMI less critical.