Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification
This provides a more efficient method for tuning neural networks in NLP tasks, but it is incremental as it applies an existing Bayesian optimization technique to a specific domain.
The paper tackled the problem of optimizing hyperparameters for neural networks in dialog act classification, demonstrating that using Gaussian processes improves results and reduces computational time by a factor of 4 compared to random search.
Systems based on artificial neural networks (ANNs) have achieved state-of-the-art results in many natural language processing tasks. Although ANNs do not require manually engineered features, ANNs have many hyperparameters to be optimized. The choice of hyperparameters significantly impacts models' performances. However, the ANN hyperparameters are typically chosen by manual, grid, or random search, which either requires expert experiences or is computationally expensive. Recent approaches based on Bayesian optimization using Gaussian processes (GPs) is a more systematic way to automatically pinpoint optimal or near-optimal machine learning hyperparameters. Using a previously published ANN model yielding state-of-the-art results for dialog act classification, we demonstrate that optimizing hyperparameters using GP further improves the results, and reduces the computational time by a factor of 4 compared to a random search. Therefore it is a useful technique for tuning ANN models to yield the best performances for natural language processing tasks.