Generating Continuous Representations of Medical Texts
Table 2: Perplexity scores for each of the models.
['[EMPTY]', '[BOLD] LSTM', '[BOLD] ARAE (uncondit.)', '[BOLD] ARAE (condit.)']
In order to assess the performance of the system, we also train a baseline language model that consists of a 1-layer LSTM. From the results we see that while both ARAE models achieve a lower perplexity than the LSTM, the conditional ARAE performs significantly better.