ETH-DS3Lab at SemEval-2018 Task 7: Effectively Combining Recurrent and Convolutional Neural Networks for Relation Classification and Extraction
Table 2: Final parameter values and their explored ranges
['[BOLD] Parameter', '[BOLD] Final value', '[BOLD] Experiment range']
The complete architecture was replicated and trained independently several times (see in order to form an ensemble of classifiers, whose output probabilities were averaged to obtain the final probabilities for each class. We analyzed and tried several deeper and more complex neural architectures, such as multiple stacked LSTMs (up to 4) and models with 2 to 4 hidden layers, but they didn’t achieve any significant improvements over the simpler models. Conclusively, the strategy that produced the best results consisted of adequately combining the individual predictions of the single models