Implicit Argument Prediction with Event Knowledge
Table 6: Evaluation on G&C dataset.
['[EMPTY]', '[ITALIC] P', '[ITALIC] R', '[ITALIC] F1']
The original EventComp models do not perform well, which is as expected since the model is not designed to do the fill / no-fill decision and multi implicit argument prediction tasks as described above. With the fill / no-fill classifier, precision rises by around 13 points because this classifier prevents many false positives. With additional multi-arg training, F1 score improves by another 22-23 points. At this point, our model achieves a performance comparable to the much more complex G&C reimplementation GCauto. Adding entity salience features further boosts both precision and recall, showing that implicit arguments do tend to be filled by salient entities, as we had hypothesized. Again, more training data substantially benefits the task. Our best performing model, at 49.6 F1, clearly outperforms GCauto, and is comparable with the original Gerber and Chai