Entity-Enriched Neural Models for Clinical Question Answering
Table 6: Hyper-parameter values across different datasets.
['[BOLD] Dataset', '[BOLD] Entity Embedding Dim', '[BOLD] Auxiliary Task Wt.']
Most of the hyper-parameters across our models remained same: learning rate: 2e−5, weight decay: 1e−5, warm-up proportion: 10% and hidden dropout probability: 0.1. This also suggests that even after adding an auxiliary task, the proposed model doesn’t need a lot of hyper-parameter tuning.