Let’s do it “again”: A First Computational Approach to Detecting Adverbial Presupposition Triggers
Table 2: Performance of various models, including our weighted-pooled LSTM (WP). MFC refers to the most-frequent-class baseline, LogReg is the logistic regression baseline. LSTM and CNN correspond to strong neural network baselines. Note that we bold the performance numbers for the best performing model for each of the “+ POS” case and the “- POS” case.
['[EMPTY]', '[EMPTY]', 'Accuracy WSJ', 'Accuracy Gigaword', 'Accuracy Gigaword', 'Accuracy Gigaword', 'Accuracy Gigaword', 'Accuracy Gigaword', 'Accuracy Gigaword']
Overall, our attention model WP outperforms all other models in 10 out of 14 scenarios (combinations of datasets and whether or not POS tags are used). Importantly, our model outperforms the regular LSTM model without introducing additional parameters to the model, which highlights the advantage of WP’s attention-based pooling method. test did not show that WP significantly outperformed the other model in these cases (p value > 0.05).