Event Representations with Tensor-based Compositions
Table 2: Hard Similarity Results: Accuracy is the percentage of cases where the similar pair had higher cosine similarity than the dissimilar pair.
['System', 'Accuracy WP', 'Accuracy EV']
Both tensor methods outperform the baselines showing that tensors capture a richer and broader set of semantics about the events. Interestingly, the event objective is better than word prediction for all models. We believe the broader context used by the event prediction objective helps capture broader event semantics necessary to generalize to the difficult pairs.