Graph based Neural Networks for Event Factuality Prediction using Syntactic and Semantic Structures
Table 1: Test set performance. * denotes the models trained on separate datasets while ** indicates those trained on multiple datasets. †specifies the models in Rudinger et al. (2018) that are significantly improved with BERT.
['[EMPTY]', 'FactBank MAE', 'FactBank  [ITALIC] r', 'UW MAE', 'UW  [ITALIC] r', 'Meantime MAE', 'Meantime  [ITALIC] r', 'UDS-IH2 MAE', 'UDS-IH2  [ITALIC] r']
This section evaluates the effectiveness of the proposed model for EFP on the benchmark datasets. We compare the proposed model with the best reported systems in the literature with linguistic features Lee et al. ; Stanovsky et al. et al. Importantly, to achieve a fair comparison, we obtain the actual implementation of the current state-of-the-art EFP models from Rudinger et al. Following the prior work, we use MAE (Mean Absolute Error), and r (Pearson Correlation) as the performance measures.