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With reference to the research paper entitled Sentiment Embeddings with Applications to Sentiment Analysis, I am trying to implement its sentiment ranking model in Python, for which I am required to optimize the following hinge loss function:

$$\operatorname{loss}_{\text {sRank}}=\sum_{t}^{T} \max \left(0,1-\delta_{s}(t) f_{0}^{\text {rank}}(t)+\delta_{s}(t) f_{1}^{\text {rank}}(t)\right)$$

Unlike the usual mean square error, I cannot find its gradient to perform backpropagation.

How do I calculate the gradient of this loss function?

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  • $\begingroup$ You will either need to contact the authors and hope at least one responds with the equation or you need to calculate it yourself. The following article might help you: twice22.github.io/hingeloss $\endgroup$ – Brian O'Donnell Oct 8 '18 at 3:44
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Hinge loss is difficult to work with when the derivative is needed because the derivative will be a piece-wise function. max has one non-differentiable point in its solution, and thus the derivative has the same. This was a very prominent issue with non-separable cases of SVM (and a good reason to use ridge regression).

Here's a slide (Original source from Zhuowen Tu, apologies for the title typo): SVM Hinge Loss equation and derivative

Where hinge loss is defined as max(0, 1-v) and v is the decision boundary of the SVM classifier. More can be found on the Hinge Loss Wikipedia.

As for your equation: you can easily pick out the v of the equation, however without more context of those functions it's hard to say how to derive. Unfortunately I don't have access to the paper and cannot guide you any further...

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