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Hi @monologg! Just a theoretical question about what the BERT for Joint Intent Classification and Slot Filling publication says here:
The learning objective is to maximize the conditional probability p(y^i, y^s|x). The model is finetuned end-to-end via minimizing the cross-entropy loss.
If I understand correctly, this is not to sum the intent and slot losses as you have in your models (total_loss = intent_loss + self.args.slot_loss_coef * slot_loss). If that part of the paper is correct, you should first multiply the probabilities calculated from both logits and then use the CrossEntropyLoss over these probabilities.
The text was updated successfully, but these errors were encountered:
Hi @monologg! Just a theoretical question about what the BERT for Joint Intent Classification and Slot Filling publication says here:
If I understand correctly, this is not to sum the intent and slot losses as you have in your models (
total_loss = intent_loss + self.args.slot_loss_coef * slot_loss
). If that part of the paper is correct, you should first multiply the probabilities calculated from both logits and then use the CrossEntropyLoss over these probabilities.The text was updated successfully, but these errors were encountered: