Given the binary nature of the classification problem we can use the cross entropy loss: where Φ is the set of all trainable weights in the model, N is the number of data samples, m i is the number of sentences in the i-th data sample, p ij is the predicted label of the j-th sentence in the i-th data sample, and y ij is the corresponding ground truth label.
ally, in an ablation study we demonstrate that our various modeling choices, which tackle the inherent challenges of comment-edit understanding, each contribute positively to empirical results.