[Paper] Rethinking Cooperative Rationalization: Introspective Extraction and Complement Control
Photo Credit Introduction Model interpretability is crucial if we want to use AI models to make high-stake decisions (e.g., making medical diagnoses, preventing suicides, etc.). In NLP, one common way to get interpretability is to extract information from the trained models. For example, some use gradient-based input attribution techniques, some perturb the input to get explanations, and some use influence functions to find the most influential training examples to this particular input sequence. Another way is to make the model intrinsically explainable (e.g., a decision tree). ...