Event Representations with Tensor-based Compositions
Table 1: Performance on the Transitive Sentence Similarity dataset, as indicated by Spearman’s ρ
['System', '[ITALIC] ρ WP', '[ITALIC] ρ EV']
The neural network baseline is only slightly better than averaging. When training for word prediction, both the predicate and the role factored tensor models have a higher correlation (+3 points in ρ). Across all models, training for the word objective is better than training for the event objective. This is to be expected since the word prediction task focuses on a narrower context, requiring that representation to be consistent with information within the sentence in which it appears. This goal aligns well with the sentence similarity task where the events that are roughly exchangeable are deemed similar.