walkingsparrow
walkingsparrow t1_j98c2qw wrote
Reply to comment by radi-cho in [R] [N] In this paper, we show how a conversational model, 3.5x smaller than SOTA, can be optimized to outperform the baselines through Auxiliary Learning. Published in the ACL Anthology: "Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task." by radi-cho
I am a bit confused. So overall, we want to make the generated response to be as close as possible to the ground truth. The paper adds a selection loss that distinguishes the generated response from the ground truth, which would make the generated response as different as possible from the ground truth. How could this help the main task of making these two responses as close as possible?
walkingsparrow t1_j9b7j3d wrote
Reply to comment by radi-cho in [R] [N] In this paper, we show how a conversational model, 3.5x smaller than SOTA, can be optimized to outperform the baselines through Auxiliary Learning. Published in the ACL Anthology: "Efficient Task-Oriented Dialogue Systems with Response Selection as an Auxiliary Task." by radi-cho
I think I understand now. Thanks for the explanation.