Neural Semantic Parsing by Character-based Translation: Experiments with Abstract Meaning Representations
Table 2: Parameter settings of the seq2seq model.
['[BOLD] Parameter Layers', '[BOLD] Value 2', '[BOLD] Parameter RNN type', '[BOLD] Value brnn']
We train a model with bidirectional encoding and general attention Luong et al. Since training a full model takes two to three days on a GPU, we perform a heuristic parameter search instead of an exhaustive one. We started out with a default model and changed only one parameter value in separate experiments. If we improved over the default, the setting was kept and combined with other parameter settings that improved performance. All models were only tested on the development set. All our described models in this paper are trained with these settings. Training is stopped 3 epochs after there is no improvement in validation perplexity on the development set anymore. The best performing model on the development set is then used to decode the test set.