Shallow Discourse Parsing Using Distributed Argument Representations and Bayesian Optimization
Table 1: Hyperparameters selected by Spearmint from the provided ranges, for non-explicit discourse relations
['[BOLD] Hyperparameter', '[BOLD] Range', '[BOLD] Best']
This algorithm samples from the space of hyperparameters, while trying to learn a function from hyperparameters to overall performance. We use the cross-entropy (and not the F1) as the overall performance objective; due to the time cost for training the model, we could run only twenty samples, which took several days. More samples may have yielded better performance, but this was not possible under the time constraints of the shared task.