Event Representations with Tensor-based Compositions
Table 3: CMCNC Results: Predict the held out event, given the observed events from the same document. The dataset is curated manually for coherence by removing noisy instances and context events. † denotes a statistically significant difference(α<0.05) over the best competing baseline under a paired t-test.
['System', 'CMCNC WP', 'CMCNC EV', 'MCNC WP', 'MCNC EV']
Whereas the word-based objective excelled on similarity tasks, the event-based objective does better on cloze tasks. The Role Factor model shows significant improvements in accuracy compared to the neural network model (+4.4 points with word prediction and +3.6 points with event prediction). The Predicate tensor model, however, performs worse than the neural network model. Our Role Factor model excels in both.