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Systems based on artificial neural networks (ANNs) have achievedstate-of-the-art results in many natural language processing tasks. AlthoughANNs do not require manually engineered features, ANNs have manyhyperparameters to be optimized. The choice of hyperparameters significantlyimpacts models' performances. However, the ANN hyperparameters are typicallychosen by manual, grid, or random search, which either requires expertexperiences or is computationally expensive. Recent approaches based onBayesian optimization using Gaussian processes (GPs) is a more systematic wayto automatically pinpoint optimal or near-optimal machine learninghyperparameters. Using a previously published ANN model yieldingstate-of-the-art results for dialog act classification, we demonstrate thatoptimizing hyperparameters using GP further improves the results, and reducesthe computational time by a factor of 4 compared to a random search. Thereforeit is a useful technique for tuning ANN models to yield the best performancesfor natural language processing tasks.

Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification