Currently, we use a simple color density approach to visualize importance. Early feedback suggests this is helpful for the user immediately see the most important words/tokens, but does not offer quantities or further interaction (e.g. top n). Can we make this better or provide better alternatives?
[ ] convert explanations to a single modal view: switcher between visualization ntypes
[ ] bar + density visualization for easier comparisons similar to what was done here?
[ ] Top n important words: show only highlights for the top x most important words?
More Explanation Methods
Currently, explanations are based on vanilla gradients. We might want to explore:
Improved Explanation Visualization
Currently, we use a simple color density approach to visualize importance. Early feedback suggests this is helpful for the user immediately see the most important words/tokens, but does not offer quantities or further interaction (e.g. top n). Can we make this better or provide better alternatives?
More Explanation Methods
Currently, explanations are based on vanilla gradients. We might want to explore:
Useful Resources
https://www.tensorflow.org/tutorials/interpretability/integrated_gradients
See https://github.com/experiencor/deep-viz-keras
https://keras.io/examples/vision/grad_cam/
https://blog.fastforwardlabs.com/2020/06/22/how-to-explain-huggingface-bert-for-question-answering-nlp-models-with-tf-2.0.html
[ ] add this as a config.yaml option, and front end toggle too?