Document Informed Neural Autoregressive Topic Models with Distributional Prior
Table 2: Deep Variants (+ Full-softmax) with T200: PPL and IR (i.e, IR-precision) for short and long text datasets.
['[BOLD] data', '[BOLD] DeepDNE  [BOLD] PPL', '[BOLD] DeepDNE  [BOLD] IR', '[BOLD] iDeepDNE  [BOLD] PPL', '[BOLD] iDeepDNE  [BOLD] IR', '[BOLD] DeepDNEe  [BOLD] PPL', '[BOLD] DeepDNEe  [BOLD] IR', '[BOLD] iDeepDNEe  [BOLD] PPL', '[BOLD] iDeepDNEe  [BOLD] IR']
We report a gain of 10.7% (402 vs 450) in PPL due to iDeepDNEe over the baseline DeepDNE, on an average over 11 datasets. Observe that the introduction of both pre-trained embedding priors and contextual information leads to improved performance on the IR task for short-text and long-text datasets. We report a gain of 11.1% (.60 vs .54) in precision on an average over the 15 datasets, compared to DocNADE. vs .59) in precision over the 11 datasets, compared to DeepDNE.