# Exploring Different Dimensions of Attention for Uncertainty Detection

arXiv: https://arxiv.org/abs/1612.06549 · HTML: https://mlsanity.com/paper/1612.06549/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2016.

## Built on by (most cited first, 7 of 7)

- [Learning Disentangled Representations of Negation and Uncertainty](https://mlsanity.com/paper/2204.00511/) (2022)
- ["You might think about slightly revising the title": identifying hedges in peer-tutoring interactions](https://mlsanity.com/paper/2306.14911/) (2023)
- [Blindness to Modality Helps Entailment Graph Mining](https://mlsanity.com/paper/2109.10227/) (2021)
- [dictNN: A Dictionary-Enhanced CNN Approach for Classifying Hate Speech on Twitter](https://mlsanity.com/paper/2103.08780/) (2021)
- [Enriched Attention for Robust Relation Extraction](https://mlsanity.com/paper/2104.10899/) (2021)
- [''You should probably read this'': Hedge Detection in Text](https://mlsanity.com/paper/2405.13319/) (2024)
- [Shall We Trust All Relational Tuples by Open Information Extraction? A Study on Speculation Detection](https://mlsanity.com/paper/2305.04181/) (2023)

## Abstract (arXiv)

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.