[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."Submitted by radi-cho t3_116kqvm in MachineLearning
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.
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