[cbutner]

SentiMATE[1] looks at one of the reverse problems in a way - training an engine on commentary data - although it's not exactly what you're talking about.

I think this line of thinking could eventually lead to automated metrics for commentary evaluation, which could in turn lead to better methods than top-k/top-p for turning a bunch of sequential logits into a sentence or paragraph - basically treat it like MCTS/PUCT also.

The problem is that if you look at high-level commentary - maybe Radjabov-MVL on https://www.chess.com/news/view/2021-champions-chess-tour-fi... (I'm not the best judge, just a quick search) - it's not often possible to predict the move starting with the comment. And if you did, you might end up with very dry metrics and reverse commentary.

But this direction has a lot of potential I think, beyond just chess, into more of an algorithmic/generational support for pure NN-based language models.

[1] https://arxiv.org/pdf/1907.08321.pdf