Attention Interpretability Across NLP Tasks (1909.11218v1)

Abstract: The attention layer in a neural network model provides insights into the model's reasoning behind its prediction, which are usually criticized for being opaque. Recently, seemingly contradictory viewpoints have emerged about the interpretability of attention weights (Jain & Wallace, 2019; Vig & Belinkov, 2019). Amid such confusion arises the need to understand attention mechanism more systematically. In this work, we attempt to fill this gap by giving a comprehensive explanation which justifies both kinds of observations (i.e., when is attention interpretable and when it is not). Through a series of experiments on diverse NLP tasks, we validate our observations and reinforce our claim of interpretability of attention through manual evaluation.

Analyzing Attention Interpretability Across NLP Tasks

The paper "Attention Interpretability Across NLP Tasks" aims to reconcile conflicting views regarding the interpretability of neural attention mechanisms within NLP tasks. Attention mechanisms are employed in various NLP applications such as machine translation, sentiment analysis, and natural language inference (NLI), among others. Despite their utility, the interpretability of attention weights has led to divergent opinions. Some researchers argue that attention weights are not inherently interpretable and do not provide insights into model predictions, while others contend that attention encapsulates meaningful linguistic information.

Key Findings and Analysis

The authors undertake a comprehensive analysis across different types of NLP tasks: single sequence tasks (e.g., sentiment analysis), pair sequence tasks (e.g., NLI and question answering), and generation tasks (e.g., neural machine translation). The investigation is aimed at understanding whether attention weights indeed correlate with the importance of input features and whether altering these weights affects model performance.

1. Single Sequence Tasks:
   • Attention methods in single sequence tasks, such as text classification, exhibited a limited impact on model output when attention weights were altered. This reduced impact is attributed to the attention mechanism functioning as a gating unit, effectively similar to controlling the flow of relevant information, thus offering no deeper insight into feature importance.
2. Pair Sequence and Generation Tasks:
   • In contrast, for pair sequence and generation tasks, perturbations to attention weights significantly degraded model performance. This finding suggests that the attention mechanism, in these contexts, does not simply act as a gating unit but rather encodes genuine dependencies between input sequences and contributes to the model's predictive power.
3. Empirical Validation via Performance Metrics:
   • Experimental results demonstrate that uniform or randomly permuted attention weights affect performance minimally in single sequence tasks but cause substantial drops in accuracy and BLEU scores in pair sequence and generation tasks. For instance, uniform attention in NLI tasks reduced accuracy by over 40 percentage points, highlighting its essential role.
4. Self-Attention in Transformer Models:
   • The paper also extends to self-attention mechanisms used in Transformer-based models. Permuting attention weights in these architectures consistently leads to decreased performance across tasks, indicating that attention plays a crucial role beyond just facilitating information flow.
5. Manual Evaluation for Interpretability:
   • Human evaluation of attention weights showed that, in single sequence tasks, attention weights are less indicative of important inputs compared to pair sequence tasks, reinforcing the conclusion that attention interpretability varies significantly across different task structures.

Implications and Future Directions

The paper's findings imply that the interpretability of attention varies according to the task and the nature of input sequences. This variation in interpretability suggests that while attention mechanisms may not provide uniform insights into model operations, they are indispensable in capturing interrelations in more complex tasks, such as sequence generation and pairs-of-sequence functions.

Future work could involve investigating alternative methods to enhance interpretability across these varied task domains. Moreover, exploring hybrid attention mechanisms that adapt dynamically based on task requirements could improve both performance and explainability.

Concluding Remarks

This paper provides a nuanced understanding of attention interpretability, emphasizing its dependency on task-specific demands. By interrogating the interpretability of attention mechanisms in a systematic manner, the authors contribute to a deeper comprehension of how neural networks leverage attention across diverse NLP applications. The investigation underscores the necessity for care when interpreting attention in neural models, urging researchers to consider task complexity and structure before drawing conclusions about model reasoning processes.