Dialog Context Language Modeling with Recurrent Neural Networks
Table 1: Language modeling perplexities on SwDA corpus with various dialog context turn sizes (K).
['[BOLD] Model', '[BOLD] K=1', '[BOLD] K=2', '[BOLD] K=3', '[BOLD] K=5']
K value indicates the number of turns in the dialog. Perplexity is calculated on the last turn, with preceding turns used as context to the model. To analyze the best potential gain that may be achieved by introducing linguistic context, we compare the proposed contextual models to DACLM, the model that uses true dialog act history for dialog context modeling. This gives a positive hint that the proposed contextual models may implicitly capture the dialog context state changes.