What are the best practices for visualizing and interpreting the attention weights in RNNs?

2 How to visualize attention?

Visualizing attention comes in many forms, depending on the type and structure of the attention mechanism, and the format and size of the data. Heatmaps, for example, are matrices of color-coded values that show the intensity of the attention weights between each pair of input and output tokens. They can be used to compare different attention layers, models, or examples, and to spot outliers or anomalies. Alignment plots are graphs that reveal connections between the input and output tokens based on the attention weights, useful for demonstrating correspondence and alignment between the input and output sequences. Attention maps overlay the attention weights on the input or output data, such as text, speech, or images; they can be used to highlight regions or features that the model is paying attention to as well as to show relevance or saliency of the data.

3 What to look for in attention?

When visualizing attention, you should consider key aspects such as coverage, diversity, and consistency. Coverage refers to the degree to which the model pays attention to all relevant parts of the input or output data without ignoring or repeating any information. Diversity is the degree to which the model attends to different aspects or dimensions of the data without focusing on only one or a few factors. Consistency is the degree to which the model attends to the same or similar parts of the input or output data across different examples or scenarios without changing its attention patterns. Ideally, a good attention mechanism should have high coverage, diversity, and consistency in order to reflect the underlying structure and logic of the data.

4 How to improve attention?

If you find that your attention mechanism is not performing optimally or is not producing meaningful or interpretable attention weights, you may want to consider some strategies to improve it. This could include changing the attention type or function, such as dot-product, additive, multiplicative, or self-attention. Additionally, regularization or constraints, like dropout, entropy, or coverage penalties, can be added to the attention mechanism to prevent overfitting or underfitting of data and reduce spurious or noisy attention weights. Furthermore, incorporating prior knowledge or domain information into the attention mechanism can help guide or influence its attention weights; for example, by using positional embeddings, syntactic features, or semantic labels.

5 How to use attention for explanation?

Finally, attention can be used as a tool for explanation, to justify or rationalize model predictions or outputs. However, caution must be taken when using attention for explanation, as it may not always reflect the true causal or contributory factors behind the model's behavior. To ensure the validity and reliability of attention weights, they should be validated against other sources of evidence such as human judgments, alternative models, or ablation studies. Additionally, attention weights should not be isolated as explanations and should be contextualized within the whole model and data to account for interactions and dependencies with other components and factors. Lastly, attention weights should be communicated clearly and accurately to acknowledge their limitations and uncertainties.