Given the hidden representation (h 1 , h 2 , . . . , h n ), it is possible to use the hidden vector corresponding to the anchor word h k as the features to perform factuality prediction (as done in<cite> (Rudinger et al., 2018)</cite> ). However, despite the rich context information over the whole sentence, the features in h k are not directly designed to focus on the import context words for factuality prediction. In order to explicitly encode the information of the cue words into the representations for the anchor word, we propose to learn an importance matrix A = (a ij ) i,j=1..n in which the value in the cell a ij quantifies the contribution of the context word x i for the hidden representation at x j if the representation vector at x j is used to form features for EFP.