The current state-of-the-art approach for EFP has involved deep learning models <cite>(Rudinger et al., 2018</cite> ) that examine both syntactic and semantic information in the modeling process. However, in these models, the syntactic and semantic information are only employed separately in the different deep learning architectures to generate syntactic and semantic representations. Such representations are only concatenated in the final stage to perform the factuality prediction. A major problem with this approach occurs in the event mentions when the syntactic and semantic information cannot identify the important structures for EFP individually (i.e., by itself). In such cases, both the syntactic and semantic representations from the separate deep learning models would be noisy and/or insufficient, causing the poor quality of their simple combination for EFP.