Neural networks with attention have proven effective for many natural
language processing tasks. In this paper, we develop attention mechanisms for
uncertainty detection. In particular, we generalize standardly used attention
mechanisms by introducing external attention and sequence-preserving attention.
These novel architectures differ from standard approaches in that they use
external resources to compute attention weights and preserve sequence
information. We compare them to other configurations along different dimensions
of attention. Our novel architectures set the new state of the art on a
Wikipedia benchmark dataset and perform similar to the state-of-the-art model
on a biomedical benchmark which uses a large set of linguistic features.Comment: accepted at EACL 201