Exploring Different Dimensions of Attention for Uncertainty Detection
This work addresses uncertainty detection in natural language processing, offering incremental improvements through novel attention architectures.
The paper tackled uncertainty detection by generalizing attention mechanisms with external and sequence-preserving attention, achieving new state-of-the-art results on a Wikipedia benchmark dataset and competitive performance on a biomedical benchmark.
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.