Year: 2017
Type: conference-paper
Abstract: 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.
DOI: 10.18653/v1/e17-1003
Authors: Heike Adel, Hinrich Schütze
Institution: Ludwig-Maximilians-Universität München
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Deutsche Forschungsgemeinschaft
OpenAlex ID: W2579589574