What are examples of simple gradient based NLP models?

I am looking for a simple example of gradient-based methods for NLP. More specifically I am looking for post-hoc local explanations gradient-based methods, that is to say, which explain a single prediction by performing additional operations (after the model has emitted a prediction). Here is an example of a gradient-based method for "Finance"? (I'm not very sure of what domain it is as it is not NLP, nor Computer Vision, nor time series ...): Let us consider a minimal example, where a linear regression is used to estimate the future capital asset $y_{c}$ , based on two investments $x_{1}$ and $x_{2}$ . Let assume the assumptions above are met and the model parameters are estimated as follows: $$\mathbb{E}\left[y_{c} \mid x_{1}, x_{2}\right]=1.05 x_{1}+1.50 x_{2},$$ We can derive immediately a global interpretation of this model. Every dollar invested in fund $x_{1}$ will produce capital of $1.05 \\\$$ , while every dollar invested in $x_{2}$ will produce a capital of $1.50 \\\$$ , independently of the values $x_{1}$ and $x_{2}$ might assume in a concrete scenario. Notice that this explanation is purely based on the learned coefficient $w_{1}=1.05$ and $w_{2}=1.50$ . These, sometimes called partial regression coefficients, are themselves candidate attribution values to explain the influence of the independent variables of the target variable: $$R_{1}(x)=1.05 \quad R_{2}(x)=1.50$$ Notice also that the coefficients are the partial derivatives of the target variable with respec…