Link?

Paper: https://aclanthology.org/2022.icnlsp-1.2/
GitHub: https://github.com/radi-cho/RSTOD

Just linked it in a top-level comment.

First author is a high-school student. Impressive.

In Bulgaria, no less.

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?

Thanks for the interest! You can follow me on Twitter: https://twitter.com/radi_cho

That it's Bulgaria is probably why it's possible at all. Notice 'high school of mathematics'.

Some ex-Soviet/ex-Warsaw pact countries have functioning maths education.

About the intuition that it would produce responses further from the human ones (in fact, we see that for this variant, the BLEU is lower) - in a way, it could work as a regularization to produce more diverse responses and prevent some overfitting. That loss mostly affects the additional head's weights which are removed during inference, but we also multiply it by an optimal constant to be sure it doesn't affect the whole architecture too much. I've sent you a PM if you wish to receive some more details or empirical insights.

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I think I understand now. Thanks for the explanation.