[Paper Review] Exploring Different Dimensions of Attention for Uncertainty Detection

Heike Adel, Hinrich Schütze|arXiv (Cornell University)|Dec 20, 2016

Topic Modeling33 references4 citations

TL;DR

This paper introduces novel attention mechanisms—external attention and sequence-preserving attention—for uncertainty detection in NLP. By leveraging external cue lexicons and preserving sequence order during attention computation, the proposed models outperform prior approaches, achieving a new state of the art on the Wikipedia benchmark with a 3.5+ F1-point improvement over the previous SOTA.

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.

Motivation & Objective

• To address the challenge of detecting uncertain (non-factual) information in text, such as speculation, opinion, or ambiguity.
• To extend the design space of neural network attention mechanisms beyond standard internal, weighted, sequence-agnostic approaches.
• To evaluate the impact of different attention dimensions—source, weighting, and sequence preservation—on uncertainty detection performance.
• To apply convolutional and recurrent neural networks with novel attention mechanisms to uncertainty detection for the first time.
• To establish a new state of the art on the CoNLL2010 Wikipedia benchmark and demonstrate strong performance on a biomedical dataset.

Proposed method

• Proposes external attention, where attention weights are computed based on an external lexicon of uncertainty cue phrases rather than internal network representations.
• Introduces sequence-preserving attention mechanisms that maintain the relative order of input tokens during attention computation, unlike standard weighted average attention.
• Integrates attention mechanisms into both convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for sequence classification.
• Uses feed-forward layers to compute attention scores, with attention weights derived from external cue lexicons to guide focus on uncertainty indicators.
• Employs a multi-layer architecture combining convolutional or recurrent encoding with attention, followed by classification head for binary uncertainty prediction.
• Conducts extensive ablation studies across different attention configurations, including internal vs. external, weighted vs. unweighted, and sequence-agnostic vs. sequence-preserving selection.

Experimental results

Research questions

• RQ1 How do external attention mechanisms, which use a predefined lexicon of uncertainty cues, affect performance in uncertainty detection compared to standard internal attention?
• RQ2 To what extent does preserving sequence order during attention computation improve performance on uncertainty detection tasks?
• RQ3 How do different attention configurations—weighted vs. unweighted, internal vs. external, sequence-agnostic vs. sequence-preserving—affect model performance across diverse NLP tasks?
• RQ4 Can convolutional and recurrent neural networks with novel attention mechanisms achieve state-of-the-art results on uncertainty detection benchmarks?
• RQ5 How do the proposed attention mechanisms generalize beyond the Wikipedia benchmark, particularly on a more complex biomedical dataset?

Key findings

• The proposed external attention mechanism significantly improves performance by guiding the model to focus on known uncertainty cues, leading to a 3.5+ F1-point improvement on the Wikipedia benchmark.
• Sequence-preserving attention mechanisms help maintain critical syntactic and semantic order, enabling better distinction between sentences like "it is not uncertain that X is Basque" and "it is uncertain that X is not Basque".
• The CNN with external attention achieved a new state of the art on the CoNLL2010 Wikipedia benchmark, outperforming previous SOTA models by more than 3.5 F1 points.
• The model performed competitively on the biomedical benchmark, matching the performance of a state-of-the-art system that relies on a large set of linguistic features.
• The ablation study confirms that external attention and sequence preservation are key contributors to performance gains, with external attention providing the most significant boost.
• The code and models are publicly released to support reproducibility and future research in uncertainty detection and attention mechanism design.